Databricks Remote

Remote Databricks Jobs

35 jobs found

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All (261) 🌍 Worldwide (52) 🇺🇸 US (202) 📍 Location-specific (7)

1d ago

Principal Data Scientist Insights & Intelligence

Northrop Grumman Remote 🌍 Remote

Remote

1d ago

$114k - $171k/yr Full time Analytics Artificial Intelligence Big Data

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Job Description

Northrop Grumman's Insights & Intelligence division is recruiting a Principal Data Scientist to drive analytical programs and deliver production-grade data science solutions that inform engineers, program managers, and leaders. This remote-hybrid role emphasizes translating business challenges into rigorous analytics and strategic insights across the organization.

Responsibilities

Collaborate with engineers, program managers, and subject-matter experts to scope problems, frame the right analytical questions, and translate business needs into robust data science strategies
Break down complex problems with critical thinking, evaluate data quality and relevance, challenge assumptions, and design methods that address the business objective
Develop statistical models, machine learning solutions, and analytical frameworks that yield actionable insights and guide operational decisions
Build user-friendly, production-ready ML/AI applications (for example Streamlit, Dash) and analytic artifacts that deliver insights to teams across the enterprise
Write production-grade Python code and assemble analytics pipelines on cloud platforms (AWS, Databricks) to support scalable, reproducible workflows
Deliver insights through executive recommendations, analytical reports, interactive dashboards, and direct discussions with business leaders
Own the technical quality and business impact of your work by making thoughtful methodological choices, validating approaches, and standing behind recommendations
Stay current on analytical methods, statistical techniques, and domain-specific best practices to continually improve quality and impact

Requirements

At least five years of hands-on experience in data science, data analysis, or related professional roles
Strong proficiency with Python, SQL, and Git
Solid understanding of statistical methods, machine learning algorithms, and selecting appropriate analytical techniques
Experience developing and deploying machine learning models in production environments
Proven ability to translate complex business problems into rigorous analytical frameworks
Demonstrated problem-solving and critical-thinking skills to tackle complex analytical challenges
Excellent communication skills with the ability to present actionable recommendations to non-technical stakeholders
Proven ownership and accountability for technical decisions and project outcomes

Technologies

Python
SQL
Git
Streamlit
Dash
AWS
Databricks
PySpark
Docker

Benefits

Health insurance coverage
Life and disability insurance
Savings plan
Company paid holidays
Paid time off for vacation and/or personal matters
Overtime eligibility
Shift differential eligibility
Discretionary bonus
Annual bonuses
Long-term incentives

Relocation assistance

No relocation assistance available

Clearance required for start

None

Clearance type

None

Travel

Yes, 10% of the time

Work arrangement

This is a hybrid/remote role. The majority of the team is based in the Northern Virginia area, but the organization operates primarily remotely and values flexibility. The standard schedule is a 9/80, with nine-hour days Monday through Thursday and every other Friday off.

Salary

USD 113,900.00 - 170,900.00 per year

Location

Remote (hybrid)

Preferred qualifications

Experience with AWS and Databricks for data processing and model development
Experience with PySpark for large-scale data transformation and analytics
Proven experience building and deploying web-based visualization or decision-support tools (Streamlit, Dash)
Knowledge of MLOps concepts and best practices for deploying models to production
Understanding of containerization and cloud-based deployment
Familiarity with advanced analytical techniques such as causal inference, experimental design, time series forecasting, optimization, or Bayesian methods
Domain experience in program management, business management, operations research, earned value management, or financial forecasting
Background in consulting or client-facing technical roles where ambiguous business problems were translated into technical solutions

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8d ago

Azure Data Engineer – AI & Data Platform

Intone Networks Remote 🌍 Remote

Remote

8d ago

Full time Artificial Intelligence Azure Azure Data Factory

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Job Description

Intone Networks is seeking an experienced Azure Data Engineer to join its remote team. The role centers on building cloud data platforms, scalable data pipelines, and AI-driven solutions that elevate data engineering productivity and deliver actionable business insights through Azure services.

Role Summary

The successful candidate will design, construct, and optimize scalable data pipelines, integrate diverse data sources, and harness Azure AI and Generative AI technologies to boost engineering efficiency and business insights.

Responsibilities

Data Engineering & Platform Development: architect and implement scalable ingestion, transformation, and ETL/ELT pipelines using Azure data services. Build and refine data solutions with Azure Data Factory, Azure Synapse Analytics, Microsoft Fabric, Azure Databricks, and Azure Data Lake Storage. Develop batch and real‑time processing using Spark, SQL, and Python. Apply enterprise‑aligned data modeling to meet business requirements.
AI & Generative AI Enablement: create AI‑assisted capabilities for automatic data pipeline generation, code acceleration, and notebook creation. Integrate Azure OpenAI and Azure AI Services into data workflows. Build intelligent data quality monitoring, anomaly detection, and predictive analytics. Design AI powered agents for operational monitoring, notifications, and workflow automation. Assess and adopt emerging AI technologies to improve engineering productivity.
Data Governance & Quality: implement data quality frameworks, validation rules, and monitoring mechanisms. Ensure security, privacy, and governance compliance. Establish metadata management, lineage tracking, and data cataloging processes. Support audit, regulatory reporting, and governance requirements.
Operational Support: monitor and troubleshoot data pipelines and platform issues. Perform root cause analysis and implement preventive measures. Support production deployments, release management, and operational excellence initiatives. Collaborate with cross‑functional teams to resolve data and platform issues.

Requirements

SQL
Azure Data Factory (ADF)
Azure Synapse Analytics
Microsoft Fabric
Azure Databricks
Azure Data Lake Storage (ADLS)
Python
PySpark
Data Modeling and Data Warehousing
Azure DevOps (ADO), CI/CD
Git
Version Control
REST APIs and Data Integration
AI & Advanced Skills
Azure OpenAI Service
Azure AI Services
Generative AI and Large Language Models (LLMs)
AI Agents and Workflow Automation
Retrieval-Augmented Generation (RAG)
Prompt Engineering
Intelligent Monitoring and AI-driven Operations

Technologies

SQL
Azure Data Factory (ADF)
Azure Synapse Analytics
Microsoft Fabric
Azure Databricks
Azure Data Lake Storage (ADLS)
Spark
Python
PySpark
Azure DevOps (ADO)
Git
REST APIs
Azure OpenAI Service
Azure AI Services
Generative AI
Large Language Models (LLMs)
AI Agents
Retrieval-Augmented Generation (RAG)
Prompt Engineering

Role Summary

We are seeking an accomplished Azure Data Engineer with a focus on cloud data platforms, modern data engineering practices, and AI‑driven solutions. The role involves designing, building, and optimizing scalable data pipelines, integrating enterprise data sources, and leveraging Azure AI and Generative AI technologies to enhance productivity and extract meaningful business insights.

Key Competencies

Problem Solving & Analytical Thinking
Stakeholder Management
Collaboration & Communication
Innovation & Continuous Improvement
Operational Excellence
AI Adoption & Automation Mindset

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10d ago

Principal Data Scientist

Risepoint Remote 🌍 Remote

Remote

10d ago

Full time Analytics Artificial Intelligence Big Data

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Job Description

Risepoint is building an AI-powered platform to improve student outcomes across a network of universities. As a Principal Data Scientist, you will lead end-to-end AI/ML initiatives, data engineering, and production delivery to build predictive models and the Next Best Experience platform, driving retention, engagement, and enrollment across more than 100 university partners. This is a remote role.

Responsibilities

Set direction for and lead AI/ML initiatives end-to-end—scoping ambiguous business opportunities, defining the problem and success criteria, designing the technical approach, managing implementation, and driving outcomes—coordinating across Product, Engineering, CX, Partnership, and university partner teams.
Own accountability for delivering measurable business outcomes from each initiative: retention lift, engagement improvement, enrollment conversion, and pipeline efficiency.
Drive alignment and decision-making across teams at each stage of an initiative’s lifecycle, resolving moderately complex, cross-functional problems independently and proactively while escalating only when tradeoffs require leadership decision.
Identify and scope net-new AI/ML opportunities that deliver impact for students, university partners, and Risepoint’s business; frame options, recommend a path forward, and advocate for prioritization with leadership.
Manage relationships with key vendors and software providers as a workstream leader, ensuring delivery commitments are met.
Influence peers, managers, and senior stakeholders across BT and adjacent business functions—including Partnership and Customer Experience—by translating technical tradeoffs into business implications and building support for shared decisions without direct authority.
Build and deploy predictive models—including churn risk, engagement propensity, and success likelihood—that power proactive student outreach and are monitored continuously in production.
Lead the design and implementation of “next best action” logic in close partnership with Product and CX, from logic design through production deployment.
Prototype, test, and productionize models using MLOps frameworks (Databricks, MLFlow, dbt, Dagster), owning the full model lifecycle.
Own clean, reliable data pipelines and feature stores that support model development and production deployment at scale, doubling as the data engineer for the workstream.
Work with speech analytics and structured CRM/LMS data to derive behavioral insights across the student lifecycle.
Architect, build, and own scalable, reliable data pipelines and the underlying data infrastructure (lakehouse, warehouse, and feature stores) end-to-end—operating as the team's principal data engineer.
Design and maintain data models, ELT/ETL workflows, and feature pipelines that serve both analytics and production model-serving needs.
Take models to production and keep them healthy there: own packaging, deployment, serving, versioning, and the full production lifecycle, including rollback.
Automate production workflows with orchestration tools (Dagster, Airflow) for scheduling, dependency management, and pipeline reliability.
Implement CI/CD pipelines and infrastructure-as-code (Terraform, Docker, Kubernetes) to automate testing, deployment, and reproducible environments.
Build automated monitoring and observability—data-quality checks, model and data drift detection, alerting, and automated retraining triggers—to keep production systems running with minimal manual intervention.
Own data quality, governance, lineage, and cost/performance optimization across the platform, setting the engineering standards the team builds against.
Design and lead A/B testing programs to measure model-driven impact on retention, engagement, and satisfaction, owning the decision to ship, iterate, or stop.
Establish feedback loops and real-world performance monitoring frameworks that enable continuous model improvement.
Translate complex technical findings into clear, executive-ready narratives that drive cross-functional alignment and action.
Mentor data scientists and engineers across the team and raise the organization’s technical bar through code reviews, pair work, and knowledge-sharing.
Model ownership, adaptability, and technical leadership in a fast-changing environment; set the standard for what it means to own a domain end-to-end.
Define technical approaches and promote technical best practices across teams, including standards for data lineage, traceability, and explainability that support user trust and regulatory needs.
Champion a continuous-learning environment, driving adoption of and experimentation with the latest AI-assisted coding and collaboration tools to multiply team velocity.
Influence the data science and AI roadmap as the technical expert and thought leader to Product and Engineering leadership.
Predictive models are deployed, monitored, and demonstrably improving student outcomes (e.g., reduced churn, higher engagement rates)—and you can point to specific initiative decisions you made that drove those results.
Cross-functional partners describe you as a principal-level technical leader who owns outcomes, not just analysis—independently resolves moderately complex problems, builds alignment across functions, manages implementation, and delivers results.
Experiment programs are well-designed, velocity is high, and a clear percentage of tests yield statistically significant outcomes that inform production decisions.
The data foundation is materially stronger because of your workstream ownership: pipelines are cleaner, features are better documented, and the team ships faster.
You actively raise the organization’s technical standard, establishing best practices others adopt, and mentoring data scientists and engineers toward greater ownership and impact.

Requirements

A proven track record of delivering measurable consumer and business impact through AI/ML initiatives—scoping, managing implementation, and owning outcomes end-to-end.
Experience operating as a principal-level technical leader or domain authority: independently resolving moderately complex, ambiguous problems; setting direction for AI/ML programs; and delivering outcomes across teams in a cross-functional environment.
8+ years in applied machine learning or data science, ideally in education, consumer tech, personalization, or a complex behavioral domain.
Strong background in predictive analytics, recommendation systems, and experimentation (A/B testing, causal inference, uplift modeling).
Deep expertise in Python and SQL; proficiency with ML libraries (scikit-learn, XGBoost, TensorFlow, or PyTorch).
Experience with Databricks, MLFlow, dbt, and Dagster—or demonstrated ability to ramp quickly on a modern MLOps stack.
Principal-level data engineering experience: architecting and operating production data pipelines, data models, and feature stores at scale.
Hands-on experience taking models to production and operating them there—deployment, serving, monitoring, and retraining.
Proficiency with production automation tooling: workflow orchestration (Dagster, Airflow), CI/CD, infrastructure-as-code (Terraform), and containerization (Docker, Kubernetes).
Strong grounding in data quality, governance, lineage, and observability practices.
Comfort working with complex, multi-source datasets (CRM, LMS, communication logs, speech analytics).
Excellent communicator and influencer across technical and non-technical audiences, including peers, managers, executives, and business partners outside BT; you make the science accessible without losing rigor and build support for decisions through evidence, clarity, and trust.
Bachelor’s or Master’s degree in a technical discipline (computer science, statistics, econometrics, mathematics, or engineering).

Technologies

Python
SQL
scikit-learn
XGBoost
TensorFlow
PyTorch
Databricks
MLFlow
dbt
Dagster
Airflow
Terraform
Docker
Kubernetes

How Impact Will Be Measured

Business outcomes tied to model-driven initiatives: retention rates, re-engagement rates, enrollment completion, and conversion lift.
Initiative delivery: on-time scoping, cross-functional execution, and outcome realization against defined success metrics.
Model performance metrics: accuracy, precision, recall, and AUC across deployed models; degradation alerts and retraining cadence.
Production reliability and automation: pipeline uptime, data-quality SLAs, deployment frequency, and reduction in manual intervention.
Experiment velocity and signal rate: number of A/B tests shipped per quarter and percentage yielding statistically significant, actionable results.
Qualitative feedback from Product, Engineering, CX, Partnership, and Customer Experience partners on initiative ownership, communication quality, cross-functional influence, and effectiveness in resolving moderately complex problems independently.

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10d ago

Applied Health Data Scientist with advanced analytics experience

innoVet Health, LLC Remote 🌍 Remote

Remote

10d ago

$140k+/yr Full time Ai Ml Analytics Artificial Intelligence

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Job Description

Applied Health Data Scientist to support national AI initiatives across federal healthcare, with a focus on the Department of Veterans Affairs. Design and evaluate ML and LLM based solutions, build reproducible pipelines, assess data quality, and translate analytical findings into actionable, governance-aware insights.

Responsibilities

Design and deploy end-to-end machine learning pipelines, covering data ingestion, preprocessing, feature engineering, model selection, training, evaluation, and production deployment, with rationale for design choices and tradeoffs.
Perform structured evaluations of LLM and agentic AI approaches, assessing safety, hallucinations, robustness, and suitability for federal healthcare use cases.
Apply statistical and ML methods in Python and SQL to analyze large healthcare datasets and validate use cases.
Create reproducible analyses with version control (git) and experiment tracking (MLflow).
Audit incoming health data sources (EHR, claims, operations) for missingness, inconsistencies, structural issues, and bias; document principled data handling decisions.
Collaborate with data engineering and system owners to improve upstream data quality, metadata, and readiness for AI/ML workloads.
Operate in secure government cloud environments (AWS GovCloud, Azure Government) and distributed compute platforms (Databricks, Spark).
Contribute to federal AI governance activities, including model documentation and risk assessments, with involvement in internal reviews or oversight processes.
Design evaluation plans to support ongoing monitoring, drift detection, and re-validation of AI systems in production.
Ensure work aligns with explainability, fairness, privacy, and evolving federal AI policy, standards, and responsible AI guidance.
Collaborate with VA stakeholders to gather and refine requirements for advanced analytics and AI initiatives.
Translate modeling results into clear visualizations and narratives tailored to technical, clinical, and executive audiences.
Embed AI solutions into existing clinical and operational workflows to minimize burden and maximize adoption.
Manage multiple concurrent projects across VA and other federal health clients, balancing deep technical work with stakeholder engagement and deliverable timelines.
Prepare formal federal deliverables such as technical memos, evaluation reports, model cards, data management plans, and reproducibility documentation.
Develop clear, defensible analyses suitable for audit, external review, and transition into federal environments.

Requirements

Master's degree in Data Science, Statistics, Computer Science, or a related quantitative field.
5+ years of hands-on experience in applied data science or machine learning, with a track record of delivering work in real world, production, or contract environments.
Proficiency in Python and SQL, including comfort with T-SQL and Databricks; R is valued for candidates from research or biostatistics backgrounds.
Experience analyzing large and complex datasets; healthcare data, especially VA healthcare data, is preferred but not required.
Familiarity with distributed computing platforms (Databricks, Spark) and secure government cloud environments (AWS GovCloud, Azure Government), or equivalent commercial cloud experience (AWS, Azure, GCP).
Familiarity with data science and ML lifecycle frameworks (CRISP-DM, OSEMN, TDSP) and the ability to structure work using governance checkpoints and documentation practices.
Exposure to large language models (LLMs) and agentic AI approaches, with the ability to evaluate potential use cases and limitations.
Ability to clearly interpret and present results to both technical and non-technical audiences.
Eligibility to obtain VA suitability and a federal PIV badge; U.S. Citizen or Green Card holder.
No 1099, corp-to-corp, or international outsourcing.

Technologies

Python
SQL, T-SQL
Databricks, Spark
AWS GovCloud, Azure Government, GCP
git, MLflow
R
CRISP-DM, OSEMN, TDSP
LLMs, agentic AI concepts
CI/CD, containerization

Benefits

401(k)
401(k) matching
Dental insurance
Health insurance
Paid time off
Referral program
Vision insurance

Job Type

Full-time

Pay

From USD 140,000 per year

Application Questions

This position involves government contracts; under Order 11935, U.S. Citizenship or permanent residency is required. Answer 2 if you are a US citizen, 1 if you have a permanent resident card.
Please provide the link to your LinkedIn account.
Please provide the link to your GitHub account.
How many years of experience do you have working on federal contracts?
How many years of experience do you have with building, evaluating, and deploying ML models in production?
How many years of experience do you have in using and evaluating Gen AI and Agentic AI solutions?

Work Location

Remote

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10d ago

Sr Data Scientist

Amgen Thousand Oaks, CA 🇺🇸 Remote

Thousand Oaks, CA

10d ago

$134k - $182k/yr Full time Senior Analytics Big Data

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Job Description

Sr Data Scientist role at Amgen in Thousand Oaks, CA (remote) offering USD 134,168 to 181,522 per year.

Responsibilities

Design, implement, and maintain data pipelines, curated datasets, analytical data products, and reporting layers using Python, SQL, and cloud platforms
Develop dashboards and visual analytics to monitor External Supply performance, manufacturing status, inventory, supply risk, supplier performance, procurement activity, and operational trends
Apply statistical, analytical, and data science methods to identify patterns, generate insights, support scenario analysis, and improve business decision-making
Collaborate with External Supply stakeholders to define requirements, clarify business problems, identify data sources, and deliver fit-for-purpose digital and analytical solutions
Integrate and analyze structured and unstructured data from enterprise systems, including ERP, supply planning, procurement, manufacturing, quality, finance, and external partner data sources
Automate recurring reports and manual analyses to improve efficiency, consistency, and data accessibility
Partner with IT and business teams to design scalable, maintainable, and reusable data architecture
Support data governance, data quality, documentation, and validation practices for analytics used in a regulated pharmaceutical environment
Communicate analytical findings, technical recommendations, and business insights clearly to technical and non-technical audiences
Promote best practices for code development, dashboard design, data management, analytics lifecycle management, and digital product sustainability

Requirements

Doctorate degree
Master's degree and 2 years of Data Scientist experience
Bachelor's degree and 4 years of Data Scientist experience
Associate's degree and 8 years of Data Scientist experience
High school diploma or GED and 10 years of Data Scientist experience

Technologies

Python
SQL
Databricks
AWS
Azure
Google Cloud
Tableau
Power BI
SAP
MES
LIMS
Veeva
Git

Benefits

Competitive benefits package including a retirement and savings plan with company contributions, medical, dental, and vision coverage, life and disability insurance, and flexible spending accounts
Discretionary annual bonus program, with sales-based incentives for field representatives
Stock-based long-term incentives
Award-winning time-off programs
Flexible work models where feasible

Application Deadline

Amgen does not set a fixed deadline; applications are accepted until a suitable candidate is selected or the team determines the pool is sufficient.

Sponsorship

Sponsorship for this role is not guaranteed.

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11d ago

Senior Data Analyst - Post Payment Analytics

Ibility Remote 🌍 Remote

Remote

11d ago

Full time Senior Analyst Analytics

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Job Description

Ibility is seeking a Senior Data Analyst to lead post-payment analytics within its healthcare data practice. This remote, full-time position focuses on transforming complex healthcare claims data into actionable insights that inform payment integrity initiatives and data-driven decision-making. The role collaborates with program leadership, business stakeholders, and technical teams to translate analytical findings into operational improvements using Databricks and a broad set of analytics tools.

Position overview

The Senior Data Analyst – Post Payment Analytics analyzes healthcare claims data to support post-payment analytics, payment integrity initiatives, and data-driven decision-making. This role works with program leadership, business stakeholders, and technical teams to convert intricate claims information into insights that enhance operational performance, identify payment trends and anomalies, and ensure compliance with data governance standards.

Responsibilities

Analyze large and complex healthcare claims datasets to support post-payment analytics, payment integrity initiatives, and program objectives.
Develop, maintain, and optimize analytical datasets, reports, dashboards, and visualizations using Databricks and other analytics tools to support business and operational needs.
Collaborate with program managers, functional leads, technical teams, and government clients to gather, interpret, and translate business requirements into actionable analytical solutions.
Perform advanced data analysis to identify payment trends, anomalies, overpayments, and opportunities for operational improvement while ensuring data accuracy and integrity.
Validate, reconcile, and monitor healthcare claims data to ensure consistency, completeness, and compliance with established data governance standards.
Provide analytical expertise and recommendations to stakeholders by presenting findings, communicating insights, and supporting data-driven decision-making.

Requirements

Bachelor's degree in Data Analytics, Computer Science, Information Systems, Healthcare Informatics, Statistics, Mathematics, or a related field. Equivalent combination of education and relevant experience may be considered.
Minimum of 4 years of experience performing data analytics, data analysis, or business intelligence activities.
Minimum of 3 years of hands-on experience developing analytical solutions using Databricks.
Demonstrated experience working with healthcare claims data, including the analysis, validation, and interpretation of large and complex datasets.
Experience developing reports, dashboards, data visualizations, and analytical products to support business operations and executive decision-making.
Strong analytical and problem-solving skills with the ability to identify trends, anomalies, and opportunities for process improvement.
Experience translating business requirements into analytical solutions and presenting technical findings to both technical and non-technical stakeholders.
Knowledge of data governance principles, data quality management, and best practices for maintaining data integrity.
Excellent written and verbal communication skills, with the ability to effectively collaborate across cross-functional teams and government stakeholders.
Ability to manage multiple priorities, meet deadlines, and work independently in a fast-paced, client-focused environment.

Technologies

Databricks
Power BI
Tableau
SQL
Python

Benefits

Health, Dental, and Vision Plans
Short Term Disability / Long Term Disability / Life Insurance / Accidental Death and Dismemberment
Health Savings Account
Unlimited Paid Time Off
11 Federal Holidays Off
401K

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12d ago

Senior Data Engineer

Optum Eden Prairie, MN 🇺🇸 Remote

Eden Prairie, MN

12d ago

$92k - $164k/yr Full time Senior Azure Data Factory Big Data

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Job Description

The Senior Data Engineer role at Optum focuses on designing, developing, and maintaining enterprise ETL pipelines for Medicaid analytics and the enterprise data warehouse, leveraging Azure, Python, Spark, and cloud data technologies.

Location and Compensation

Location: Eden Prairie, MN (remote). Salary: USD 91,700 - 163,700 per year.

Responsibilities

Design, develop, and maintain enterprise ETL pipelines using Azure Data Factory, Informatica PowerCenter, and Python-based frameworks
Build and optimize scalable data processing solutions with Python, Spark, and Databricks
Support Medicaid analytics and federal reporting initiatives (T MSIS, PERM, MARS, Quality of Care)
Develop robust data validation, reconciliation, and auditable data pipelines
Write and optimize SQL and stored procedures across relational platforms such as Snowflake, Oracle, and SQL Server
Participate in cloud migration and modernization initiatives within Azure-based architectures
Collaborate with analysts, QA, and reporting teams to ensure data quality, accuracy, and timeliness
Follow data engineering best practices for performance, reliability, reusability, and security
Support production operations, incident resolution, and root cause analysis
Participate in code reviews, source control, and CI/CD processes using Azure DevOps and GitHub

Requirements

5+ years of data engineering experience with a focus on enterprise data warehousing
5+ years of hands-on ETL development using Informatica PowerCenter, Azure Data Factory, or similar tools
5+ years of Python development for data engineering and automation
5+ years of Teradata experience
3+ years of experience with Spark based processing frameworks (Databricks or equivalent)
Solid SQL expertise and experience with relational databases (such as Snowflake, Oracle, SQL Server)
Experience with source control and DevOps practices (Azure DevOps, GitHub, CI/CD)
Proven solid analytical, problem solving, and troubleshooting skills
Ability to travel periodically based on project or customer needs