Azure DevOps

Azure DevOps Data Jobs

12 jobs found

10d ago

Data Scientist II, Anti Money Laundering Transaction Monitoring

BMO Financial Group Chicago, IL

Chicago, IL

10d ago

$69k - $128k/yr Full time Ai Ml AML Aml Ctr

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Job Description

BMO Financial Group is seeking a Data Scientist II to advance its AML transaction monitoring program from the Chicago area. The role centers on the design, development, and deployment of machine learning and analytical models for detecting money laundering activity and supporting the model lifecycle, with collaboration across AML Compliance, investigations, governance, and technology teams.

Responsibilities

Design, build, test, and deploy machine learning and analytical models used for AML transaction monitoring and suspicious activity detection
Analyze large volumes of customer, transactional, payment, and alert data to identify emerging money laundering risks, typologies, and anomalous behavior patterns
Develop and evaluate supervised and unsupervised machine learning approaches to improve detection effectiveness and investigator outcomes
Conduct feature engineering and exploratory data analysis to identify behavioral indicators associated with financial crime risk
Perform quantitative assessments of model performance, including detection effectiveness, productivity, false positive reduction, and risk coverage
Support model tuning, optimization, and revalidation activities to ensure models continue to perform as intended
Assess data quality and data lineage and partner with data management teams to resolve issues affecting model performance and reliability
Prepare clear and comprehensive model development, testing, and governance documentation to support model validation, audit, and regulatory reviews
Translate analytical findings into actionable recommendations for AML Compliance, FIU, and senior management
Collaborate with business stakeholders, investigators, model governance, and technology partners to prioritize enhancements and implement solutions
Research emerging financial crime typologies, machine learning techniques, and industry best practices to continuously improve monitoring capabilities
Contribute to strategic initiatives involving AI, machine learning, graph analytics, network analysis, and other advanced analytical approaches applicable to financial crime detection
Ensure all analysis and model development activities comply with regulatory requirements, internal policies, and model risk management standards
Take measured risks while protecting the bank by applying the Risk Management Framework and exercising sound risk-based judgment

Requirements

Typically between 4 - 6 years of relevant experience and a post-secondary degree in a related field or an equivalent combination of education and experience
Experience with Python and SQL for large-scale data analysis, feature engineering, and model development
Strong understanding of statistical analysis, machine learning algorithms, and model performance measurement
Experience working with large structured and semi-structured datasets
Experience using version control tools and code repositories (e.g., Git, GitHub, Azure DevOps) to support collaborative development and reproducible analytical workflows
Familiarity with generative AI tools and AI-assisted development practices (e.g., Microsoft 365 Copilot, GitHub Copilot, LLM-based coding assistants) to improve productivity, documentation, and analytical workflows
Ability to communicate technical concepts effectively to both technical and non-technical audiences

Technologies

Python
SQL
Git
GitHub
Azure DevOps
Dataiku
Databricks
SAS
Spark
Hadoop
Microsoft 365 Copilot
GitHub Copilot
LLM-based coding assistants
Cloud-based analytics platforms

Benefits

Performance-based incentives
Discretionary bonuses
Health insurance
Tuition reimbursement
Accident and life insurance
Retirement savings plans

Application deadline

08/04/2026

Location

Chicago, IL (onsite)

Address

320 S Canal Street

Salary

USD 69,000 - 127,800 per year — Salaried

Job Family Group

Data Analytics & Reporting

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12d ago

Sr Databricks Data Engineer

Deloitte Tempe, AZ

Tempe, AZ

12d ago

$116k - $229k/yr Full time Azure Azure DevOps Big Data

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Job Description

Deloitte is seeking a Senior Databricks Data Engineer to join the AI and Data practice in Tempe, Arizona on site. This role focuses on designing, building, and optimizing cloud based data engineering solutions to modernize data platforms, enable analytics and AI use cases, and drive data driven decision making across large scale enterprise transformations. The position offers a salary range of USD 116,200 to 229,100 per year and requires a bachelor’s degree in Computer Science, Engineering, or a related field with a minimum of one year of experience.

Responsibilities

Champion Best Practices: Define, document, and promote leading approaches for data architecture, integration, and modeling.
Pipeline Ownership: Design, develop, and maintain robust data pipelines and architectures that support enterprise data needs at scale.
Drive Excellence: Lead initiatives to enhance data quality, operational efficiency, and process scalability.
Team and Technology Lead: Assess, pilot, and integrate new big data and analytics technologies; mentor data engineers and architects to ensure effective project delivery.
Data Governance: Advise on and implement governance, security, and compliance strategies for modern cloud data ecosystems.
Communication: Translate technical concepts and business value for executives, business leads, and technology teams.
DevOps and Automation: Oversee CI/CD practices with tools such as Azure DevOps, AWS Code Pipeline, Jenkins, TFS, or PowerShell to streamline deployments and operations.

Requirements

Ability to work independently and collaboratively within a team.
Strong written and verbal communication skills.
Meticulous attention to detail and quality of work.
Ability to build and maintain professional relationships.
Capacity to lead projects or workstreams.
Ability to manage multiple tasks in a fast paced and dynamic environment.
Strong interpersonal skills and professional demeanor.
Ability to meet deadlines.
Bachelor's degree in Computer Science, Engineering, or a related field.
5+ years of hands on data engineering experience focusing on Databricks on AWS, Azure, or GCP.
Experience with Lakehouse architecture, Apache Spark, Delta Lake, cloud native databases, storage solutions, and distributed compute platforms.
Experience with data warehousing, 3NF, dimensional modeling, enterprise data lakes, incremental data loads, and metadata driven ingestion and data quality frameworks using PySpark.
1+ year leading complex cross functional data projects and technical teams, including Delta Live Tables, Autoloader, Structured Streaming, Databricks Workflows, Apache Airflow, Unity Catalog, automated CI/CD pipelines, and optimization of data pipelines, code, and compute resources.
Willingness to travel up to 50 percent depending on assignment and client needs.
Limited immigration sponsorship may be available.

Qualifications Required

Bachelor's degree in Computer Science, Engineering, or a related field.
5+ years of hands on data engineering experience with a focus on Databricks on AWS, Azure, or GCP.
Experience with Lakehouse architecture, Apache Spark, Delta Lake, cloud native databases, storage solutions, and distributed compute platforms.
Experience with data warehousing, 3NF, dimensional modeling, enterprise data lakes, incremental data loads, and metadata driven ingestion and data quality frameworks using PySpark.
1+ year leading complex cross functional data projects and technical teams, with experience in Delta Live Tables, Autoloader, Structured Streaming, Databricks Workflows, Apache Airflow, Unity Catalog, automated CI/CD pipelines, and performance optimization of data pipelines and compute resources.
Ability to travel 50 percent on average based on project requirements.
Limited immigration sponsorship may be available.

Preferred

Master’s degree in Computer Science, Engineering, or a related field.
Experience across one or more cloud ecosystems (AWS, Azure, GCP) and associated big data services.
Proficiency in tuning and optimizing performance within Databricks and Apache Spark environments.
Experience with Databricks Lakeflow.
Background in artificial intelligence and machine learning solutions.

Technologies

Databricks, Delta Lake, Delta Live Tables, Autoloader, Structured Streaming, Databricks Workflows, Unity Catalog, Databricks Lakeflow
Apache Spark, PySpark
AWS, Azure, GCP
Azure DevOps, AWS Code Pipeline, Jenkins, TFS, PowerShell
Lakehouse architectures, cloud-native databases, and distributed compute platforms
Apache Airflow

The Team

Deloitte's Core AI and Data practice assists organizations in modernizing data platforms, strengthening enterprise data foundations, and scaling analytics and AI capabilities across the business. The team collaborates with clients to architect, engineer, and deploy cloud-based data solutions that improve decision making, drive innovation, and support large-scale transformation. Practitioners work across business and technology functions to solve complex data modernization challenges.

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16d ago

Azure Data Engineer – AI & Data Platform

Intone Networks Remote 🌍 Remote

Remote

16d 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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21d ago

Salesforce Technical AI Engineer (Senior Consultant, Technical Transformation)

Deloitte Arlington, VA

Arlington, VA

21d ago

$118k - $160k/yr Full time Anthropic Claude Code Artificial Intelligence Automation

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Job Description

Senior Salesforce Technical AI Engineer (Technical Transformation) at Deloitte, based onsite in Arlington, VA. This role leads the design, development, and deployment of Salesforce Public Sector solutions with GenAI integration, CI/CD, and government-client engagement in the Washington, DC region. The position requires a Bachelor’s degree and at least three years of relevant experience.

Responsibilities

Lead the end-to-end design, development, and deployment of advanced solutions using Salesforce Public Sector offerings and emerging capabilities such as Agentforce within mission-critical programs.
Establish and manage robust CI/CD pipelines and DevOps practices to ensure rapid and secure delivery.
Serve as a technical manager and hands-on innovator, embedding GenAI and modern AI practices directly into the development lifecycle to accelerate delivery and enhance system capabilities.
Work across the full technology stack, from backend integrations with large data platforms (Salesforce Data Cloud, Informatica, Databricks, Palantir Foundry) to frontend user experiences.
Collaborate directly with senior government clients, providing technical leadership and translating complex mission requirements into scalable, forward-looking solutions.

Requirements

Bachelor’s degree and a minimum of 3 years of relevant experience.
Ability to work independently as well as within a team environment.
Effective written and verbal communication skills.
Meticulous attention to detail and a commitment to high-quality work products.
Ability to build and sustain professional relationships.
Capability to lead projects or workstreams.
Proficiency in managing and prioritizing multiple tasks in a fast-paced setting.
Strong interpersonal skills and a professional demeanor.
Ability to meet deadlines consistently.
Experience mentoring others and providing clear guidance.

Technologies

Salesforce
Apex
LWC
Salesforce Data Cloud
Informatica
Databricks
Palantir Foundry
Agentforce
Slack
MuleSoft
Azure DevOps
Jenkins
GitLab
Claude Code
GitHub Copilot

Benefits

Compensation range: USD 118,000 - 160,000 per year
Discretionary annual incentive program

The Team

Deloitte's Government & Public Services GPS practice focuses on delivering impact through people, ideas, technology, and outcomes. The team serves federal, state, and local government clients as well as public higher education institutions, bringing a fresh perspective to help clients anticipate disruption, reimagine possibilities, and fulfill their mission promises. The Sales & Service offering designs and implements sales and service experiences, capabilities, and architectures required to translate customer strategies into tangible results.

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1mo ago

Full-Stack Azure Data Engineer

Telecommunications Development Corp. Laurel, MD

Laurel, MD

1mo ago Closed

Full time Azure Data Lake Storage Azure Databricks Azure DevOps

This position is no longer accepting applications (closed Jul 30, 2026).

Job Description

This in-person, full-time role is based in Laurel, MD. You will develop, deploy, and maintain cloud-native data pipelines using Azure Databricks, Synapse, and Azure DevOps.

Responsibilities

Build and scale ETL and ELT pipelines using Azure Databricks notebooks and Synapse scripts.
Implement standardized logging, error handling, and restartable processing patterns to ensure pipeline reliability.
Configure integrations with Azure Data Lake Storage, source systems, and enterprise data platforms.
Architect and maintain CI/CD pipelines and code repositories to automate environment promotion and release management.

Requirements

Strong proficiency in Python and SQL.
Proven expertise in Azure Databricks and Azure Synapse development.
Deep experience with automated deployment strategies like Azure DevOps.

Technologies

Python
SQL
Azure Databricks
Azure Synapse
Azure DevOps
Azure Data Lake Storage

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1mo ago

Senior Data Engineer

Deloitte Miami, FL

Miami, FL

1mo ago Closed

$95k - $150k/yr Full time Senior Azure Azure Data Factory

This position is no longer accepting applications (closed Jul 26, 2026).

Job Description

Senior Data Engineer in Miami, FL (onsite) at Deloitte, with a salary range of USD 95,000 - 150,000 per year and a Bachelor's degree requirement, focusing on end-to-end data solutions and ETL/ELT pipelines using Azure Data Factory and Databricks, including governance, design, development, optimization, and mentoring within the Project Delivery Model.

Responsibilities

Maintain regular communication with Engagement Managers, project teams, and cross-functional stakeholders, escalating issues that need engagement management input.
Design, develop, and optimize ETL and ELT pipelines leveraging Azure Data Factory and Databricks.
Author and tune PySpark and Spark SQL notebooks to handle large-scale data transformations.
Architect end-to-end data solutions across development, UAT, and production environments using Unity Catalog.
Steer design discussions with client architects and other counterparts to align on technical direction.
Collaborate with multiple teams on data contracts and schema agreements to ensure interoperability.
Lead the design and optimization of high-volume data pipelines.
Define and enforce data engineering standards, including naming conventions, partitioning strategies, cluster configurations, and Spark tuning.
Drive performance improvements through AQE tuning, liquid clustering, broadcast joins, and shuffle partition management.
Design Databricks cluster policies, autoscaling settings, and cost-optimization strategies.
Perform root cause analysis on production incidents and implement permanent fixes.
Mentor junior and mid-level engineers via code reviews and pair programming.
Evaluate new technologies and advise on adoption, such as Delta Apps (DABs), Delta Live Tables (DLT), Auto Loader, Serverless Compute, and Event Hubs.

Requirements

Python, PySpark, Spark SQL, and SQL Server proficiency.
Experience with Azure services including Data Factory, Data Lake Storage Gen2, Key Vault, and Azure Monitor.
Hands-on work with Databricks components such as Delta Lake, Unity Catalog, and Workflows.
Apache Airflow for workflow orchestration.
Git and Azure DevOps for version control and CI/CD.
Deep understanding of Spark internals, including DAG optimization, spill analysis, and skew handling.
Advanced Delta Lake features such as time travel, deletion vectors, and predictive I/O.
Unity Catalog governance covering row and column security, external locations, and system tables.
Infrastructure as code experience with Terraform and Azure ARM templates.
Bachelor's degree in Computer Science, Information Technology, Computer Engineering, or related IT discipline, or equivalent experience.
Limited immigration sponsorship may be available.
Ability to travel about 10 percent on average based on client needs and engagements.

Technologies

Python
PySpark
Spark SQL
SQL Server
Azure Data Factory (ADF)
Azure Data Lake Storage Gen2
Key Vault
Azure Monitor
Databricks
Delta Lake
Unity Catalog
Workflows
Apache Airflow
Git
Azure DevOps
Terraform
Azure ARM templates
DABs
Delta Live Tables (DLT)
Auto Loader
Serverless Compute
Event Hubs

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1mo ago

Machine Learning Engineer

Robert Half Los Angeles, CA

Los Angeles, CA

1mo ago Closed

$200k - $260k/yr Full time Ai Ml Artificial Intelligence Azure Databricks

This position is no longer accepting applications (closed Jul 13, 2026).

Job Description

Robert Half invites a Machine Learning Engineer to join onsite in Los Angeles, focusing on deploying and operating scalable ML infrastructure and GenAI/LLM systems. The role centers on MLOps platforms, feature stores, vector search, RAG, and CI/CD automation on Databricks. The position offers a competitive annual salary of USD 200,000 to 260,000, along with a comprehensive benefits package and opportunities for professional development.

Benefits

Medical insurance
Vision insurance
Dental insurance
Life insurance
Disability insurance
401(k) plan
Free online training

Responsibilities

Lead the design, implementation, and ongoing maintenance of scalable ML infrastructure on Databricks, including MLflow for experiment tracking, a model registry, and model serving endpoints.
Oversee the development of the ML Ops platform and automated pipelines for deploying, monitoring, and maintaining models in production environments.
Implement robust solutions for model versioning, systematic retraining, and artifact management using Databricks Unity Catalog for ML governance.
Design and manage Databricks Feature Store to ensure consistent feature engineering across training and inference pipelines.
Architect and implement Retrieval-Augmented Generation (RAG) systems for document Q&A, enabling business teams to query fund documents, investor letters, and market research.
Design, deploy, and manage vector database solutions (Databricks Vector Search, Pinecone, or similar) for semantic search and retrieval across enterprise documents.
Lead LLM fine-tuning and customization initiatives, training models like Claude or open-source alternatives with CIM proprietary data while ensuring data privacy and compliance.
Develop and optimize document processing pipelines including PDF parsing, chunking strategies, and embedding generation for RAG applications.
Implement prompt engineering best practices and LLM evaluation frameworks to ensure output quality, relevance, and factual accuracy.
Build guardrails and safety measures for GenAI applications, including hallucination detection, output validation, and source attribution.
Design and implement extensive automation across the ML workflow, covering model training, testing, validation, and deployment using Databricks Workflows and Asset Bundles.
Set up robust CI/CD pipelines for both traditional ML models and GenAI applications, leveraging GitHub Actions, Azure DevOps, or similar tools.
Automate complex data and model workflows utilizing orchestration tools such as Airflow, Prefect, or Databricks Workflows.