Top Data Engineer Jobs

Design, build, and own scalable data infrastructure for proteomics research and machine learning. Develop production data platforms, automated workflows, cloud infrastructure, pipelines, lakehouse architectures, and integrations with instruments and LIMS. Establish data integrity, lineage, security, and reproducibility standards while partnering with scientific teams. Set technical direction, operate terabyte-scale systems, review designs and code, and mentor colleagues.

Designs, develops, and operates production data pipelines and curated, AI-ready data products for AbbVie R&D. Responsibilities include data integration, normalization, modeling, metadata, lineage, quality controls, governance, documentation, knowledge graph publication, and support for RAG and vector-search workflows. The role collaborates with scientists, engineers, architects, data owners, and vendors; provides technical guidance; monitors pipeline reliability; resolves issues; and ensures compliance with data security, GxP, and regulatory standards.

Designs, builds, deploys, and maintains scalable cloud-native data platforms and pipelines in AWS. Supports data acquisition, preparation, database architecture, cloud migration, AI/ML, data science, and business intelligence initiatives. Leads collaborative teams in evaluating data, designing solutions, and managing operational data flows. Requires TS/SCI clearance with polygraph and experience in all-source analysis and data engineering.

Designs, builds, deploys, and maintains scalable cloud-native data platforms and pipelines using AWS. Supports cloud migration, data science, AI/ML, and business intelligence initiatives by organizing structured and unstructured data. Responsibilities include database architecture, data acquisition and preparation, software development, platform modernization, analytics architecture evaluation, and cross-functional team leadership. Requires TS/SCI clearance with polygraph and substantial all-source analysis experience.

Designs and develops cloud data architectures, ingestion frameworks, data models, and analytical platforms. Advises clients on cloud migration, data warehousing, technical approaches, and implementation roadmaps. Collaborates with stakeholders through requirements gathering, workshops, whiteboarding, and project meetings while deploying end-to-end cloud analytics solutions. Requires experience with cloud platforms, big data technologies, BI tools, data warehousing methodologies, programming, SQL, version control, and Agile/DevOps practices.

Gather client requirements and architect, develop, and deploy cloud data platforms, warehouses, ETL processes, pipelines, APIs, and BI solutions. Perform advanced SQL and data modeling, support deployments and integrations, diagnose technical issues, mentor staff, contribute to innovation and practice development, and assist with project management. The role requires approximately 20% overnight travel and primarily in-person work.

Own the technical direction, architecture, roadmap, and operational health of Teamfight Tactics’ data ecosystem. Build scalable batch and near-real-time data systems, telemetry, schemas, data models, governance, observability, and reliable sources of truth. Lead cross-functional initiatives, define engineering standards, optimize costs, mentor practitioners, and partner with game, product, insights, and data science teams across feature development and live operations.

Participates in a 12-month new-graduate development program while designing, building, deploying, and operating data architectures and pipelines. Develops processes for capturing, transforming, storing, and maintaining structured and unstructured data across distributed and local systems. Applies automation, big-data, artificial intelligence, and machine-learning techniques to support data integrity, compatibility, and evolving business requirements.

Own and expand Chicory’s analytical data platform supporting advertising, campaign, financial, product, and publisher reporting. Design BigQuery architecture, build batch and streaming pipelines with Python, SQL, Beam, Dataflow, and Airflow, and develop governed data marts and BI datasets. Establish data quality, observability, reconciliation, access controls, cost management, and engineering practices. Partner with engineering, finance, product, campaign management, sales, and leadership while leading incident recovery and documenting systems.

Support senior engineers in building, testing, and maintaining enterprise data pipelines and integrations. Write and optimize SQL queries, implement data quality checks, logging, and documentation, and participate in source control, code reviews, sprint activities, and support response. Complete and present an assigned data engineering project while developing practical skills in databases, scripting, ETL/ELT, and cloud data engineering.

Builds and maintains cloud-based data pipelines, transformations, semantic views, conformed dimensions, and analytical data models supporting reporting and AI/ML. Implements data quality checks, documentation, lineage, testing, release-readiness evidence, and defect resolution. Collaborates with data owners, governance, engineering, security, product, and customer teams to deliver reliable, governed data products. The role supports enterprise data modernization, migrations, DataOps, and production data platforms for a federal client.

Own production data ingestion pipelines for healthcare and benefits data, including normalization, mapping, validation, reconciliation, observability, and secure handling of sensitive information. Unify census data processes, scale the platform, improve identity matching, and build automation and TypeScript tooling. The role requires strong Python and SQL skills, production orchestration experience, reliable testing and data-quality practices, and effective communication with technical and nontechnical stakeholders.

Develops and supports enterprise reporting, dashboards, data pipelines, and analytics solutions using SSRS, Tableau, OBIEE, SQL, and related technologies. Translates business requirements into governed reporting products, optimizes queries and data integrations, validates results, resolves production issues, and maintains documentation. Collaborates with data engineering, governance, security, business, and data science teams across cloud, on-premises, and hybrid environments while supporting data quality, compliance, and self-service analytics.

Develop and maintain Databricks notebooks, workflows, GitLab repositories, and foundational CI integrations. Troubleshoot access controls and Python library issues, integrate Databricks, GitLab, Jira, and Confluence APIs, and analyze project activity data. Establish merge request and code review standards, support DevSecOps practices, evaluate new technical tools, and provide technical guidance to combatant commands and cross-functional engineering teams. The role is onsite at Andrews AFB three days weekly and remote two days.

Build and maintain Databricks data pipelines, ETL/ELT processes, SQL transformations, and Power BI dashboards for HR and People Analytics. Ensure data quality, optimize models and reporting performance, troubleshoot issues, document solutions, and collaborate with HR stakeholders and technical teams to deliver scalable workforce analytics.

Designs, builds, and maintains scalable data platforms and resilient pipelines using Medallion architecture, Databricks, Apache Spark, Parquet, Iceberg, and Delta Lake. The role includes database optimization for PostgreSQL and SQLite, data quality and performance monitoring, ETL/ELT development, troubleshooting, documentation, and collaboration with analytics stakeholders.

Build scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.

Build and maintain recruiting analytics data infrastructure using BigQuery, dbt, Fivetran, SQL, and Python. Design dimensional models, ETL/ELT pipelines, semantic layers, governance controls, data security, monitoring, and standardized recruiting metrics. Integrate Workday, Greenhouse, and internal systems while supporting batch and real-time analytics. Partner with data scientists, engineers, recruiters, and business stakeholders to deliver trusted data products and AI-enabled workforce insights.

Lead technical design and hands-on development of Pantheon’s data platform, extending ingestion, curation, retention services and ML/LLM infrastructure. Mentor engineers, drive architecture and engineering standards, operate in a DevOps model, partner cross-functionally, and participate in on-call support to ensure reliable, compliant data access and analytics.

Develop and maintain systems for investment manager data ingestion, cleaning, validation, reporting, and performance attribution. Establish data engineering standards, improve platform efficiency, adopt new technologies, and train team members. Collaborate with analysts, technologists, investors, and partner teams to build reliable data solutions and shape data and attribution practices.

Build and support production data pipelines, Databricks workflows, dimensional models, semantic models, and enterprise integrations using Azure, SQL, Python, and PySpark. Own solutions from requirements through deployment, monitoring, and support. Investigate data discrepancies, improve data quality, modernize legacy systems, and assess downstream impacts on Power BI and business metrics. Partner with stakeholders across healthcare and business functions, mentor technical teammates, and establish engineering standards for testing, CI/CD, documentation, and governance.

Develop and support Palantir Foundry data ontologies, automated data integration, predictive analytics, and proactive decision-making capabilities for Aegis missile defense system engineering. Collaborate with federal engineers, Naval Officers, and technical teams to deliver missile defense capabilities. The role requires missile systems experience, engineering or technical education, active Secret clearance, and familiarity with collaboration and project-management tools.

Build and operate production clinical-trial data pipelines using Python, PySpark, and SQL. Transform diverse source data into internal models, develop quality checks and reconciliation, investigate data issues, and improve observability. Partner with clinical subject matter experts and customer-facing teams to translate domain knowledge into reliable pipeline logic. Deliver customer-driven changes under enterprise SLAs while maintaining engineering standards through testing, code review, documentation, and CI/CD.

Design, develop, and maintain scalable data pipelines, models, databases, and data infrastructure using ETL and big-data technologies. Cleanse, integrate, and validate large datasets; implement data governance; troubleshoot complex data issues; and collaborate with analysts, scientists, and stakeholders. Develop and enhance software supporting mission requirements, maintain application databases, and coordinate hardware-software integration. The role requires onsite work in Arlington, Virginia, an active Secret clearance, and U.S. citizenship.

Provides hands-on technical leadership for foundational data engineering and analytics engineering. Owns multi-year data architecture, production pipelines, dimensional models, and trusted metrics spanning cloud costs, traffic, infrastructure, and AI usage. Designs new datasets integrating cost, utilization, performance, and reliability signals; establishes standards, coaches engineers, aligns stakeholders, and drives adoption across Infrastructure, Finance, Data Science, and Product Engineering.