Top Data & Analytics Jobs

Owns the data transformation layer powering agency reporting. Builds reliable analytical models, dimensional schemas, reporting architecture, monitoring, and alerting. Translates government and customer reporting requirements into reusable data models and tools, supports embedded analytics and recurring imports, and ensures authorization scoping prevents cross-organization data exposure. Partners across product and implementation teams while establishing data engineering practices and documentation.

Leads data architecture and engineering for the META initiative, owning solution design, data modeling, pipeline standards, code reviews, mentoring, performance tuning, and roadmap decisions. Requires deep AWS data engineering expertise, PySpark, SQL, infrastructure as code, and CI/CD experience.

Build and optimize batch and streaming data pipelines on Google Cloud for analytics and machine learning. Develop Python, SQL, Beam, and Spark transformations; deliver curated and ML-ready datasets; implement data quality, governance, security, monitoring, and schema management; optimize BigQuery; maintain Airflow DAGs; and manage Terraform-based infrastructure and CI/CD. Collaborate with architects, data scientists, ML engineers, and stakeholders while troubleshooting pipeline and data quality issues.

Build and optimize batch and streaming data pipelines for analytics and AI workloads using GCP services, Python, SQL, Beam, and Spark. Develop ML-ready datasets, implement data quality, governance, security, monitoring, and schema management, and optimize BigQuery performance. Maintain Airflow DAGs, Terraform infrastructure, and CI/CD pipelines. Troubleshoot production issues and collaborate with architects, data scientists, ML engineers, and stakeholders. The role requires U.S. citizenship, a bachelor’s degree, Public Trust eligibility, and 3–6+ years of experience.

Develop scalable data pipelines, high-performance SQL queries, reports, dashboards, and analytics solutions for a federal law enforcement client. Integrate disparate data sources, use relational and cloud databases, support evolving stakeholder requirements, and communicate technical solutions to clients and cross-functional teams. The role also involves data governance, quality improvement, mentoring, technical leadership, and consulting engagement. Employment is contingent upon contract award and requires U.S. citizenship plus eligibility for DHS 6C Public Trust.

Designs and builds data models, schemas, and ETL/ELT pipelines for analytics, reporting, and machine learning workloads. Optimizes query performance and cloud data infrastructure, establishes data quality and governance practices, integrates datasets with BI and machine learning tools, documents workflows, and mentors junior engineers. Collaborates with data scientists, analysts, and product owners to translate business requirements into scalable technical solutions.

Engineers and maintains enterprise telemetry ingestion pipelines, edge processing, data routing, filtering, masking, tagging, and transformation solutions. Develops Splunk technology add-ons, resolves parsing and timestamping issues, establishes secure transport pathways, optimizes storage and licensing, and documents data flows and taxonomies. Supports platforms including TAK, TRAX, Compass, CDS, and cloud environments while enabling threat hunting, compliance reporting, and DoD data visibility requirements.

Designs, builds, deploys, and maintains scalable data platforms and ETL/ELT pipelines. Processes structured, unstructured, batch, streaming, and event-based data while optimizing data lake architecture and cloud performance. Collaborates with analysts, engineers, developers, and data consumers on mission-driven projects. Responsibilities include data modeling, governance, cataloging, lifecycle management, automation, platform tuning, and developing data solutions for applications such as fraud detection, cancer research, and national intelligence.

Build and maintain Snowflake and dbt data pipelines, dimensional models, integrations, and Python workflows. Own data quality, testing, monitoring, documentation, performance, and production support. Translate stakeholder needs into reliable data products, develop internal tools and APIs, and follow Git, CI/CD, testing, and release practices. Work independently within Scrum teams, resolve escalated issues, contribute to standards, and mentor junior engineers.

Entry-level data platform engineer responsible for building and maintaining SQL and dbt transformations, Python workflows, ETL/ELT pipelines, integrations, automated tests, and data-quality checks. The role includes monitoring and troubleshooting pipelines, supporting applications and production issues, documenting technical processes, and collaborating with senior engineers, analysts, and business stakeholders. The position offers mentorship while developing skills across Snowflake, cloud data services, software development, and data operations.

Leads the design and development of Azure and Databricks-based data engineering solutions for ingesting, transforming, storing, and distributing big data. Oversees engineering teams, sets technical strategy, mentors junior staff, develops REST APIs hosted in Kubernetes, performs testing, and collaborates with Product, Data Science, Architecture, and business stakeholders.

Lead complex data engineering engagements, manage stakeholders and risks, coordinate offshore teams, and build scalable Azure data pipelines. Develop ingestion and transformation solutions using Azure Databricks, Data Factory, Spark, Delta Lake, SQL, and Python. Harmonize raw data into consumer-ready formats, query large datasets, support on-premises cloud migration, and collaborate with executives and delivery teams.

Own end-to-end data engineering for a large-scale analytics and machine learning platform. Build ingestion, modeling, transformation, and workflow pipelines; integrate external systems; ensure reliability, scalability, and data quality; and support production machine learning workflows. Develop programmatic interfaces for AI applications and manage analytics engineering within a cloud data warehouse.

Build and optimize batch and streaming data pipelines for AI, analytics, and enterprise platforms. Develop scalable transformations across cloud and lakehouse environments, implement data modeling, lineage, metadata, quality controls, and orchestration, and resolve complex performance and reliability issues. Partner with cross-functional teams to deliver secure, governed data solutions while mentoring engineers and leading modern data engineering initiatives.

Own the architecture and operation of 9amHealth’s data platform, including Python and SQL ETL/ELT pipelines, AWS infrastructure, Redshift data models, semantic layers, governance, quality, monitoring, and reliability. Establish technical standards, optimize performance and costs, support AI/ML initiatives, build integrations, and mentor data team members. Partner with business and clinical stakeholders to deliver reliable data products and translate complex questions into actionable analytics.

Build and maintain external market data ETL pipelines and warehouses using Snowflake, Databricks, Sybase, Azure, dbt, and Python. Troubleshoot data access and performance issues, optimize data consumption for cost and reliability, design Azure data delivery channels, and coordinate deployments with infrastructure teams. The role also contributes to backend database development, data architecture, operational excellence, testing, and performance optimization within financial services.

Lead delivery for analytics and data platform initiatives as a hands-on player-coach. Build and refine Bronze-to-Silver-to-Gold Medallion transformations, develop performant data models, improve pipeline reliability, and implement complex Spark and Databricks solutions. Coordinate contractors, plan sprints, review pull requests, manage dependencies, and communicate progress and risks. Support a GDPR-first, multi-region data strategy and collaborate with engineering leadership on data warehouse unification.

Design, build, deploy, and maintain scalable data pipelines and platforms in production. Process structured and unstructured data through batch and streaming systems, optimize lakehouse and warehouse environments, and implement data quality, testing, and observability practices. Collaborate with analysts, developers, and data consumers in agile teams to deliver mission-driven solutions. The role may support fraud detection, cancer research, and national intelligence initiatives.

Own and evolve WorkOS’s internal data platform, including ingestion, orchestration, Snowflake, dbt models, data governance, reverse ETL, semantic views, and AI-enabled data workflows. Ensure pipeline reliability, freshness, security, and scalability while partnering with Product, Finance, RevOps, GTM, Engineering, and Security. Build monitoring, runbooks, CI/CD gates, access controls, masking policies, and infrastructure automation across AWS and Kubernetes.

Design and develop scalable backend services, APIs, data ingestion solutions, ETL workflows, and data pipelines. Perform data modeling, mapping, and large-scale file manipulation while optimizing systems for speed and scalability. Collaborate in an Agile environment, apply LLMs to enrich data, and work with distributed systems, NoSQL databases, cloud platforms, and AI tooling. An active TS/SCI clearance with full-scope polygraph is required.

Lead end-to-end delivery of data products on Databricks, translating business requirements into technical designs, data models, builds, tests, and releases. Develop Bronze, Silver, and Gold-layer solutions using PySpark, Spark SQL, Delta Lake, Unity Catalog, Python, SQL, and Git-based CI/CD. Manage offshore engineering teams and vendors through backlog planning, design reviews, code reviews, and delivery oversight. Partner with business stakeholders on data definitions, quality, governance, and acceptance criteria.

Build and maintain production data ingestion, transformation, orchestration, and modeling pipelines using Dagster or Airflow, dbt, Snowflake, Python, Airbyte, and AWS. Monitor and debug production workflows, manage backfills and incremental loads, configure infrastructure, review code, and document runbooks. Partner with analytics and product teams on dimensional modeling, data quality, and pipeline reliability.

Designs and develops Databricks and cloud-based data solutions, reliable data pipelines, automated testing, and governed datasets. Collaborates with data scientists, product partners, architects, and engineers to deliver scalable analytics solutions. Responsibilities include mentoring junior engineers, improving data engineering practices, participating in Agile planning and retrospectives, and ensuring data quality, metadata documentation, and downstream usability.

Design and architect scalable healthcare data systems, core data models, and production data pipelines. Integrate and normalize healthcare data using TUVA, OMOP, and FHIR standards; establish data quality and governance; optimize performance; deploy secure cloud infrastructure; ensure regulatory compliance; and build bi-directional EMR integrations. The role also requires independent technical decision-making, test automation, CI/CD, and DevOps practices in a fast-paced healthcare technology startup.

Build and scale secure data lakehouse and warehouse infrastructure supporting analytics, machine learning, clinical AI performance monitoring, and self-service insights. Develop scalable data pipelines, models, orchestration, quality controls, and LLM-enabled analytics tools using heterogeneous clinical and EHR data. Collaborate with engineering, data science, clinical, product, and client success teams to establish trustworthy metrics and technical solutions.