Top Data Engineer Jobs

Designs and operates an AWS-native lakehouse supporting analytics, reporting, visualization, and AI/ML. Builds batch and streaming ingestion pipelines using Python and PySpark, Apache Iceberg data structures, governance and metadata services, quality controls, security, observability, and CI/CD automation. Optimizes performance, scalability, reliability, and cost while collaborating with cross-functional technical teams and documenting architecture, operations, and secure configurations.

Supports secure data pipelines, ETL/ELT workflows, data transformations, and analytics-ready datasets in a classified DoD environment. Collaborates with data scientists, engineers, and mission stakeholders while using Python, SQL, Spark, Databricks, and comparable platforms. Performs data validation, modeling, quality practices, testing, documentation, and Agile development. The role is fully on-site in a SCIF at Camp Smith, Hawaii, and requires an active Top Secret/SCI clearance before starting.

Designs, builds, deploys, and maintains scalable data platforms, warehouses, lakes, ETL/ELT pipelines, and batch or streaming workflows. Optimizes cloud data lake and pipeline performance, manages data governance and lifecycle solutions, and collaborates with multidisciplinary agile teams. The role supports mission-driven client projects involving disparate structured, unstructured, and event-streaming data.

Lead design, build, and operate scalable Databricks/Delta Lake data pipelines and Kimball dimensional models using dbt. Manage governance with Unity Catalog, optimize performance and cost, operationalize ML with MLflow, and coordinate/quality-check offshore vendor engineering. Mentor engineers, uphold standards, and deliver trusted data products that power analytics, reporting, and MLOps workflows.

Lead the technical design and operation of consumer-facing data products on a Databricks lakehouse. Own medallion architecture, dimensional modeling, ODCS data contracts, data quality, observability, CI/CD, incident response, and production standards. Write and review Python and SQL, guide code reviews, mentor engineers, collaborate with product, platform, governance, and ML teams, and ensure compliant, reliable delivery in a regulated insurance environment.

Designs and maintains scalable batch and streaming data pipelines, data warehouses, cloud data platforms, validation frameworks, and governance processes. Supports analytics through data modeling and Tableau enablement, resolves complex data issues, delivers architecture prototypes, collaborates with business and audit stakeholders, owns projects end to end, and mentors engineers. Requires advanced SQL and Python, AWS, Snowflake or Databricks, orchestration, transformation tooling, regulatory compliance, and extensive data engineering experience.

Designs, builds, and maintains scalable ETL/ELT pipelines, cloud data infrastructure, storage, and batch or real-time processing workloads. Optimizes queries, PySpark jobs, pipelines, performance, and cloud costs. Partners with BI, analytics, data science, and AI teams to develop dimensional data marts supporting reporting and machine learning. Implements governance, data quality validation, lineage tracking, security controls, and automated infrastructure and code deployments using CI/CD practices.

Build and optimize scalable data pipeline architecture, assemble complex datasets, improve data delivery infrastructure, and support data initiatives across engineering, product, BI, and data science teams. The role requires production-scale software engineering experience focused on data and ML processes, expertise with Spark, Kafka, SQL, and a programming language such as Python, Scala, or Java, plus knowledge of databases, data modeling, and ML operations.

Provides senior technical leadership for enterprise data engineering platforms, with emphasis on Snowflake, ETL/ELT pipelines, data quality, governance, observability, reliability, and production operations. The role guides architecture and engineering standards, supports incident response and on-call operations, improves platform scalability and resilience, and partners with engineering, infrastructure, security, governance, architecture, and business teams. It is a hands-on leadership position without direct people-management responsibility.

Designs, develops, and maintains Databricks-based ETL pipelines, data integration solutions, data warehouses, and reporting datasets. Builds batch and streaming architectures using Python, Spark, SQL, and Delta Lake; optimizes performance and costs; migrates legacy SSIS and SQL Server processes; maintains SSRS reports; implements data quality controls; and collaborates with business and technical teams supporting analytics and AI/ML workloads.

Designs, builds, and optimizes scalable end-to-end data pipelines and curated data products using Snowflake, SQL, and Python. Translates business needs into reliable technical solutions, ensuring data quality, governance, performance, and cost efficiency. Troubleshoots complex issues, contributes to architectural decisions, uses AI-assisted engineering tools, and collaborates with engineering, analytics, product, and business stakeholders. The role also includes code reviews, documentation, mentoring, and influencing data engineering best practices.

Develop and manage data pipelines, integrations, analytics solutions, ontologies, business object models, and enterprise data architectures using Palantir Foundry. The role supports data governance, quality, lineage, operational workflows, technical solution design, and cross-functional collaboration with technical and business stakeholders. Federal or Department of Defense experience and Agile experience are preferred. A DOD Secret clearance is required.

Designs, builds, and operates enterprise data lakes, warehouses, lakehouses, ETL/ELT pipelines, databases, and reporting platforms. Prepares governed, model-ready data for AI and machine learning, supports feature stores and embedding workflows, administers relational databases, optimizes queries and dashboards, and ensures data quality, security, availability, and disaster recovery. Collaborates with technical and business teams, reports to senior management, and mentors data engineers.

Designs, develops, and maintains Databricks-based ETL pipelines, data lake architectures, datasets, and data warehouses supporting reporting, analytics, and AI/ML workloads. Troubleshoots and optimizes Databricks and SSIS processes, maintains SSRS reports, implements data quality controls, migrates legacy systems, and collaborates with business and technical teams. The role also promotes AI-assisted development practices and documents technical procedures.

Build and operate production data and streaming systems across edge, hybrid, OT, IoT, and enterprise environments. Responsibilities include developing connectors, real-time event-driven pipelines, data contracts, schemas, validation, lineage, observability, telemetry, federated query patterns, and reliable integration platforms supporting analytics and AI. The role requires strong ownership, system design, documentation, incident readiness, and collaboration with engineering and customer-facing teams.

Design, build, deploy, and operate production-grade data pipelines and products for healthcare and marketing datasets. Implement data models, Airflow workflows, data-quality controls, and PHI/PII privacy controls. Collaborate with product, analytics, and platform teams to support audience discovery, segmentation, activation, measurement, and reporting.

Build, deploy, and operate production-grade data pipelines and data products for healthcare audiences. Design transformations, data models, and governed views using Python, SQL, Airflow, S3, Snowflake, and EMR. Implement data-quality, monitoring, and privacy-by-design controls for PHI/PII. Partner with product, analytics, and platform teams to onboard sources, support audience discovery, segmentation, activation, measurement, and troubleshoot production issues.

Designs, analyzes, documents, integrates, and migrates complex environmental datasets for AirHub business lines. Responsibilities include analyzing legacy databases, creating logical and physical data models, documenting mappings and business rules, developing migration specifications and API integrations, supporting data governance and testing, and producing technical documentation. The role also requires close collaboration with business stakeholders in an Agile delivery environment.

As a Data Engineer, you will design, implement, and maintain data systems for Notifications at Whatnot, ensuring data quality and performance while collaborating with cross-functional teams on data architecture and workflows.

Senior Data Engineer responsible for designing and deploying scalable data pipelines, ensuring critical data availability, movement, transformation, and storage. The role requires Python, Databricks, ETL, relational database, and Agile Scrum experience, supporting AI and machine learning applications for national security missions. The position is primarily onsite three to five days per week and requires an active Secret clearance and U.S. citizenship.

Lead financial data engineering initiatives involving commissions, incentive programs, and backend financial systems. Build scalable, accurate, and reliable data pipelines, platform tooling, partner integrations, analytics infrastructure, and data science infrastructure. Shape technical strategy and roadmaps, collaborate with Finance, Product, Data Science, and Engineering, mentor engineers, contribute code, lead incident response, and establish engineering standards.

Designs and leads cloud-native data engineering and architecture solutions for Supply Chain analytics. Responsibilities include building AWS data pipelines and lakehouse platforms, implementing Apache Iceberg and Medallion Architecture, developing Redshift data products, applying data mesh and governance principles, integrating SAP and Blue Yonder data, and leading technical engineering teams.

Architects and develops scalable big data pipelines, ETL processes, real-time analytics frameworks, and cloud data infrastructure for battery and time-series data. Ensures data quality, designs storage and data models, supports production AI/ML workflows, and integrates model outputs into pipelines. Translates business and engineering needs into functional specifications while coordinating internal and external stakeholders.

Develops scalable data models, Databricks pipelines, backend applications, ETL processes, and data ingestion systems. Partners with engineering, product, and business stakeholders on technical roadmaps; monitors and optimizes data systems for performance, reliability, and scalability. Provides technical leadership, mentors junior engineers, conducts code reviews, and builds secure solutions for sensitive healthcare data.

Build and operate Figma’s shared data platform supporting analytics, data science, and AI products. Responsibilities include improving development, testing, CI/CD, deployment, monitoring, data quality, incident response, performance, governance, lineage, and self-service data pipeline workflows. The role partners cross-functionally to establish reliable patterns for batch, micro-batch, reverse ETL, and product-facing data delivery.