Data Analytics

Data Analytics Jobs

1,145 jobs found

26d ago

Data Analyst Sr - Level 3

Lockheed Martin Corporation Fort Worth, TX

Fort Worth, TX

26d ago

Full time Senior Analyst Analytics

Apply Now

Job Description

Senior Data Analyst (Level 3) focusing on data analysis to support F-35 Sustainment at Lockheed Martin, leading data-set creation, modeling efforts, QA, and the development of AI, ML, and automation solutions to drive business results.

Responsibilities

Serve as the technical lead to perform analyses that define data sets used in sustainment modeling activities.
Coordinate and develop problem statements, modeling approaches, and ground rules and assumptions (GR&A).
Program manage key tasks and resources to meet cost and schedule targets.
Mine, process, and summarize data for stakeholders.
Oversee data scraping QA, query databases for stakeholder requests, and triage data quality issues for timely resolution.
Analyze large datasets, draw valid inferences, and present insights in narrative or visual formats, including dashboards.
Examine fielded performance to forecast outcomes, perform root cause analysis, evaluate potential solutions, and assess corrective actions.
Set up and maintain data processes and new analytics capabilities.
Develop AI, ML, and automation solutions.
Produce and track key performance indicators.
Translate business problems into functional and technical requirements.

Requirements

Bachelor of Science degree in Engineering or a related technical discipline (examples include Industrial, Mechanical, Aerospace, Electrical, Physics, Data Science, Data Analysis, Mathematics) with emphasis in modeling, data manipulation, and applying mathematical and statistical methods to business and risk management problems.
Eligibility to obtain a DoD Secret Clearance; US Citizenship is required for government security clearances.
Knowledge of scripting languages (Python, R, SQL) and data visualization (Tableau).
Experience with Artificial Intelligence, Machine Learning, and/or Automation.
Strong analytical skills and understanding of system databases, data elements, data structures and hierarchies, and/or application software solutions to optimize data gathering and data quality.

Technologies

Python
R
SQL
Tableau
MS Office

Benefits

Flexible work schedules
Comprehensive benefits
Competitive pay
Paid Time Off benefits

Expression of Interest

By applying to this job, you express interest in the position and could be considered for other career opportunities where similar skills and requirements are identified. If a match is identified, you may be contacted for this and future openings.

Ability to Work Remotely

Part-time remote telework: the selected candidate will work part of their schedule remotely and part of their schedule at a designated Lockheed Martin facility. The weekly schedule will be discussed during the hiring process.

Work Schedules

Lockheed Martin supports a variety of alternate work schedules to provide additional flexibility. Schedules range from standard 40 hours over five days to condensed formats that offer more time away from the office.

Schedule for This Position

4x10 hour days, three days off per week

Other Important Information

Lockheed Martin is an equal opportunity employer. Qualified candidates will be considered without regard to legally protected characteristics. The application window closes in 90 days; applicants are encouraged to apply within 5 to 30 days of the requisition posting date to receive optimal consideration.

View full job page →

26d ago

Data Engineer

Fairfax County Fairfax, VA

Fairfax, VA

26d ago

$87k - $155k/yr Full time Analytics Apache Airflow Automation

Apply Now

Job Description

Fairfax County's Department of Information Technology offers a stable, public-sector role with a strong emphasis on data-driven decision making. This onsite opportunity in Fairfax, VA provides a competitive salary range of USD 87,024 - 155,120 per year, a collaborative culture, and a chance to impact the community by delivering secure, scalable data solutions. You will work with analytics and AI teams to design and deploy robust data platforms that support decision making and innovation.

Responsibilities

Design, build, and maintain ETL pipelines using orchestration tools such as Apache Airflow and Azure Data Factory, along with big data frameworks like Spark and Hadoop, to move data from multiple sources into centralized stores like data warehouses and data lakes.
Plan and optimize database and data architectures by designing storage solutions (including PostgreSQL, SQL Server, MongoDB, Cassandra, AWS S3, Google BigQuery), covering schemas, partitioning, indexing, and data modeling for performance, scale, and cost.
Maintain data quality, governance, and security through validation rules, anomaly detection, metadata and lineage management, and adherence to compliance frameworks (GDPR, HIPAA), with encryption, access controls, and auditing in partnership with security teams.
Collaborate with business analysts, data scientists, and developers to provide optimized datasets, improve query performance, integrate APIs, and support data projects for decision-making and innovation.
Build and manage Snowflake environments, including initial setup, warehouse configuration, RBAC, data ingestion pipelines, and performance optimization to enable scalable, secure, and cost-efficient analytics.
Coordinate across cross-functional teams to support data initiatives and ensure alignment with broader BI and data efforts.
Perform additional duties as needed to sustain and advance business intelligence and data initiatives.

Requirements

Bachelor's degree in computer information systems, business administration, or a related field, plus five years of experience in information systems and process redesign, with experience managing and implementing all phases of the systems development life cycle, including at least one year in a supervisory role.
An associated criminal background check will be required and must be satisfactory to the employer.

Technologies

Apache Airflow
Azure Data Factory
Spark
Hadoop
PostgreSQL
SQL Server
MongoDB
Cassandra
AWS S3
Google BigQuery
Snowflake

Preferred Qualifications

Experience designing and maintaining ETL pipelines using Apache Airflow or Azure Data Factory.
Strong background with big data frameworks (Spark, Hadoop) and cloud storage (AWS S3, Google BigQuery).
Experience integrating external data sources via APIs or streaming frameworks into centralized repositories.
Experience building or optimizing data lakes or data warehouses, including schema design and partitioning strategies.
Experience implementing data validation rules and anomaly detection to ensure production data quality.
Experience managing metadata and data lineage to support governance.
Expertise in relational and NoSQL databases, data modeling, governance, and security best practices.
Proven ability to collaborate with analytics and AI teams, including query optimization and dataset preparation.
Strong technical communication, problem-solving, and organizational skills.
Experience working in the public sector and in large-scale data environments.

Physical Requirements

Work is generally sedentary and performed in a standard office environment. Duties may be performed with or without reasonable accommodations.

Selection Procedure

Panel interview and may include a practical exercise.

View full job page →

26d ago

Reliability Analytics Engineer

Amazon.com Services LLC Seattle, WA

Seattle, WA

26d ago

$117k - $160k/yr Full time Amazon Emr Amazon Rds Amazon Web Services

Apply Now

Job Description

Amazon.com Services LLC is seeking a Reliability Analytics Engineer to blend hands-on reliability work with a strong data systems mindset. The role is onsite in Seattle, WA, with a salary range of USD 117,300 to 160,000 per year. This position concentrates on reliability data analytics, defining data pipelines, and building tools and dashboards to support reliability across Amazon Robotics fleets, scaling evaluation across multiple product programs.

Responsibilities

Articulate data requirements and specifications for reliability pipelines, partnering with data engineering teams to design and validate ETL processes that ingest field failures, test data, and product telemetry from enterprise data lakes and EAM systems.
Cleanse, validate, and prepare reliability datasets (including censored life data, field service records, and accelerated test results) to ensure accurate assessments before they inform engineering decisions.
Translate reliability engineering questions into data queries and assessment workflows, converting ambiguous problems into repeatable, scalable evaluation methods that other engineers can reuse.
Develop and maintain reliability-focused assessment tools and automation using Python, AI/ML, and statistical libraries, such as automated failure mode classification, survival assessment calculators, and fleet health monitoring scripts.
Define dashboard requirements for reliability KPIs (availability, MTBF, MTTR, and failure rate trends); build prototypes and operationalize production dashboards.
Identify data gaps, define collection requirements for new failure modes or test programs, and collaborate with hardware test and field teams to close data gaps.
Establish data governance practices for reliability datasets, including metadata standards, version control, and traceability to source systems to ensure reproducible and auditable assessment findings.

Requirements

BS degree in mechanical engineering or equivalent
3+ years of experience in mechanical engineering or equivalent
Experience with data analysis tools such as Advanced Excel, SQL, Tableau, Python
Experience applying basic statistical methods (e.g., regression) to challenging business problems

Technologies

Python
SQL
Tableau
Advanced Excel
AWS S3
AWS Redshift
AWS EMR
AWS RDS
Reliasoft
Minitab
JMP
AI/ML

Benefits

Medical, Dental, and Vision Coverage
Maternity and Parental Leave Options
Paid Time Off (PTO)
401(k) Plan

A Day In The Life

Run Mean Cumulative Function (MCF) and Weibull analyses on robot fleets to verify suspensions before sharing results with the reliability team.
Collaborate with data engineers to define telemetry pipeline requirements, including required fields, censoring rules, and output schemas.
Develop Python tools that automate reliability growth tracking from the data lake, apply models, and generate standard reports accessible to engineers.
Provide failure rate data by subsystem to support DFMEA exercises.
Refine machine learning classifiers that auto-tag field failure tickets by mode, improving accuracy with sustaining team feedback.
Operate as a reliability engineer who understands repairable systems, physics-of-failure, and life data.
Differentiate through scaling expertise by building data tools, automation, and AI that the entire team can run。

About The Team

Amazon Robotics Reliability Engineering is a multidisciplinary group of engineers united by a drive to astonish customers by solving complex optimization challenges. The team thrives on ambiguity, moves quickly, and supports one another’s growth.

View full job page →

26d ago

Project - Data Engineer

Deloitte Bellevue, WA

Bellevue, WA

26d ago

$57k - $96k/yr Full time Analytics Azure Data Factory Big Data

Apply Now

Job Description

Deloitte is seeking a Data Engineer to join the Project Delivery Model, building and refining data pipelines that transform raw data into analytics-ready datasets on AWS and Snowflake. This onsite role based in Bellevue, WA collaborates with analysts, product owners, and source-system teams to deliver impactful data solutions, with a salary range of USD 57,300 to 95,500 per year and a bachelor’s degree as the baseline requirement.

Responsibilities

Design and enhance data pipelines on AWS using Python to ingest, transform, and deliver data to Snowflake and downstream consumers.
Create and maintain Snowflake objects (schemas, tables, views) and develop performant SQL transformations to produce curated datasets ready for analytics.
Implement workflow automation and scheduling with dependencies, retries, and logging using tools such as Airflow/MWAA, Step Functions, or Glue.
Apply data quality checks and basic observability, support incident triage and remediation, and ensure data integrity across pipelines.
Optimize pipeline and query performance with guidance on efficient Python practices, S3 partitioning and formats, and Snowflake warehouse usage and tuning.
Adhere to CI/CD and IaC standards, including Git-based workflows and Terraform or CloudFormation changes to promote code across environments.
Collaborate with analysts, product owners, and source-system teams to clarify requirements, validate outputs, and participate in sprint ceremonies and estimations.
Contribute to code reviews, unit tests, and peer debugging while applying team engineering standards and best practices.
Maintain regular communication with Engagement Managers, project team members, and cross-functional stakeholders, escalating issues as needed for engagement management.
Lead client engagement workstreams independently or collaboratively, driving process improvements, optimization, and transformation using leading practice workflows and quality improvements.

Requirements

1+ year of experience building or enhancing data pipelines and curated datasets for analytics or downstream consumption.
1+ year of hands-on SQL and Python experience, including Snowflake and/or PySpark for transformations and scalable processing.
1+ year of cloud data engineering experience on AWS (preferred) or Azure/GCP, including orchestration/scheduling (Airflow/MWAA, Step Functions, Glue, ADF/Fabric Data Factory).
Understanding of ELT patterns and Lakehouse/warehouse concepts; familiarity with S3 file formats and partitioning (Parquet/Delta).
Working knowledge of DevOps practices (Git-based workflows, CI/CD) and exposure to Infrastructure-as-Code (Terraform/CloudFormation).
Understanding of data quality, basic observability, and metadata/governance fundamentals.
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 approximately 10% depending on client engagements.

Technologies

AWS
Python
Snowflake
SQL
PySpark
Airflow
MWAA
Step Functions
Glue
Terraform
CloudFormation
Amazon S3
Parquet
Delta Lake
Azure Data Factory (ADF)
Fabric Data Factory
Git

View full job page →

27d ago

Data Engineer, Ads Science Analysis & Tech (ASAT)

Amazon.com Services LLC New York, NY

New York, NY

27d ago

$111k - $185k/yr Full time Ad Tech Analytics Big Data

Apply Now

Job Description

This entry-level Data Engineer role within the Ads Science Analysis & Tech (ASAT) group at Amazon Ads is based onsite in New York, NY. The position targets recent undergraduates and emphasizes developing analytics methods, analyzing complex data flows, and translating insights into actionable recommendations for bidding, targeting, relevance, and feature releases. The role offers a salary range of USD 111,400 to 185,000 per year. A bachelor’s degree is required, with a minimum of one year of data engineering experience.

Responsibilities

Assess data trends related to supply, optimization, ad load, and advertising mix to understand effects on advertiser performance and support advertiser goals.
Deliver research papers to senior leaders on topics such as how feature development influences identity recognition rates and how changes to ad selection systems affect fill rate, highlighting insights to guide business development and engineering roadmaps.
Define and standardize analytics for Ads auctions by examining bid spreads, auction depth, and simulating the impact of potential auction structure changes.
Identify, standardize, and operationalize KPIs to measure performance across ad serving systems, using trend insights to steer business priorities.
Collaborate with engineering teams to define data logging requirements and secure priority placement in engineering roadmaps.
Validate financial models through analysis.
Develop and manage ad revenue and supply intelligence analytics decks delivering ongoing in-depth analyses.

Requirements

At least one year of data engineering experience.
Proficiency with SQL.
Experience in data modeling, data warehousing, and building ETL pipelines.
Bachelor's degree required.
Experience using AI to automate tasks and building projects with GitHub and other sources to showcase work.

Technologies

SQL
GitHub

Benefits

Sign-on incentives
Restricted stock units (RSUs)
Comprehensive health coverage (medical, dental, vision, prescription)
Basic life and AD&D insurance
Optional supplemental life insurance plans
Employee Assistance Program (EAP)
Mental health support services
Medical advice line
Flexible Spending Accounts (FSAs)
Adoption and surrogacy reimbursement
401(k) with employer matching
Paid time off
Parental leave

A Day in the Life

The Data Engineer collaborates with business leaders and engineers to design a unified data architecture that optimizes data logging across multiple granularities, enabling interoperability from bid flow through optimization to campaign delivery. The role includes analyzing the data and preparing papers and ongoing reports that translate insights into actionable recommendations.

About the Team

The Ads Science Product Team aims to work with stakeholders who require product data, applying objective analysis and business logic to reveal insights, advise strategic decisions, and adapt to evolving industry conditions.

View full job page →

27d ago

Project - Data Engineer

Deloitte Stamford, CT

Stamford, CT

27d ago

$57k - $96k/yr Full time Amazon Web Services Analytics AWS

Apply Now

Job Description

Deloitte is seeking a Project Delivery Data Engineer in Stamford, CT on site to build data pipelines and analytics-ready datasets in AWS and Snowflake for a large onshore/offshore program.

Responsibilities

Design and optimize AWS data pipelines with Python to ingest, transform, and deliver data to Snowflake and downstream consumers.
Create and maintain Snowflake objects (schemas, tables, views) and implement performant SQL transformations to produce curated, analytics-ready datasets.
Implement workflow automation and scheduling using Airflow/MWAA, Step Functions, or Glue, including dependencies, retries, and logging.
Embed data quality checks and basic observability with validation rules, reconciliations, and alerts; support incident triage and remediation.
Improve pipeline and query performance through guidance on efficient Python practices, S3 partitioning/file formats (Parquet/Delta), and Snowflake warehouse usage and tuning.
Follow CI/CD and IaC standards with Git workflows and Terraform/CloudFormation changes to promote code across environments.
Collaborate with analysts, product owners, and source-system teams to clarify requirements and validate outputs; participate in sprint ceremonies and estimations.
Contribute to code reviews, unit tests, and peer debugging; adhere to team engineering standards.
Maintain regular communication with Engagement Managers, project team members, and cross-functional stakeholders; escalate matters requiring engagement management input.
Lead client engagement workstreams focused on improvement, optimization, and transformation of processes, implementing best-practice workflows and driving operational outcomes.

Requirements

Minimum 1 year of experience building or enhancing data pipelines and curated datasets for analytics and downstream consumers.
At least 1 year of hands-on SQL and Python experience, including Snowflake and/or PySpark for transformations and scalable processing.
1+ year of cloud data engineering experience on AWS (preferred) or Azure/GCP, including orchestration or scheduling (Airflow/MWAA, Step Functions, Glue, ADF/Fabric Data Factory).
Understanding of ELT patterns and lakehouse/warehouse concepts; familiarity with S3 file formats and partitioning (Parquet, Delta).
Working knowledge of DevOps practices (Git-based workflows, CI/CD) and exposure to Infrastructure-as-Code (Terraform or CloudFormation).
Understanding of data quality, basic observability, and metadata/governance fundamentals.
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 approximately 10 percent, depending on assignments and client needs.
Experience delivering in Agile environments.
Analytical ability to manage multiple projects and prioritize tasks into manageable work products.
Ability to work independently or with minimal supervision.
Excellent written and verbal communication skills.
Ability to deliver technical demonstrations.

Technologies

AWS, Python, Snowflake, SQL, PySpark
Airflow (MWAA), Step Functions, Glue
Terraform, CloudFormation, Git
Parquet, Delta, S3
Azure Data Factory, Fabric Data Factory

Benefits

Discretionary annual incentive program

View full job page →

27d ago

Data Engineer, Product Analytics

Meta San Francisco, CA

San Francisco, CA

27d ago

$147k - $208k/yr Full time Analytics Big Data Data Analysis

Apply Now

Job Description

Onsite in San Francisco, CA at Meta, this role offers a salary range of USD 147,000 - 208,000 per year, plus eligible bonuses and equity. You will design scalable data architectures across multiple products, collaborating with software engineers, data scientists, and product managers to optimize growth, user experience, and business decisions. The role centers on data architecture, governance, and analytics to inform product development and strategy.

Benefits

Bonus
Equity

Responsibilities

Own the data architecture for multiple large scale projects, evaluating design options and cost-benefit tradeoffs across systems
Create and improve data logging frameworks; partner with data infrastructure to triage and resolve issues
Collaborate with engineers, product managers, and data scientists to understand data needs and present key insights clearly
Define and manage service level agreements for all data sets in assigned ownership areas
Determine and implement security models based on privacy requirements, ensure safeguards, address data quality, and evolve governance processes
Design, build, and deploy sophisticated data models and visualizations that support multiple use cases across products or domains
Address challenging data integration problems using optimal ETL patterns, frameworks, and query techniques across structured and unstructured sources
Assist in owning production processes, optimizing complex code through advanced algorithmic concepts
Optimize pipelines, dashboards, frameworks, and systems to simplify the development of data artifacts
Influence product and cross-functional teams to identify data opportunities that drive impact
Mentor team members through actionable feedback

Requirements

Bachelor's degree in Computer Science, Computer Engineering, a related technical field, or equivalent practical experience
4+ years of experience working with data in roles such as data analyst, data scientist, data engineer, or similar
4+ years of experience with SQL, ETL, data modeling, and at least one programming language (e.g., Python, C++, C#, Scala)

Technologies

SQL
Python
C++
C#
Scala
ETL

About Meta

Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology.