Top Data Engineer Jobs

Design, build, and operate end-to-end ETL/ELT pipelines to ingest network telemetry into a Data Mart. Create high-performance dimensional models, implement automated data quality and observability, enforce governance and IAM, maintain metadata and lineage, and enable analysts with production-ready datasets and standardized metrics.

Design, build, and maintain scalable Spark/Python data pipelines and automated workflows. Enforce data governance, quality, lineage, and observability. Support ML pipelines (feature engineering, validation, monitoring) and document data assets, contracts, and standards.

Lead technical strategy, architecture, and modernization of enterprise master data and data lakehouse platforms. Design and implement large-scale data pipelines, databases, and distributed systems using Python, Spark, Redshift, Iceberg and AWS services. Ensure performance, reliability, governance, CI/CD, and AI-enabled engineering practices while mentoring senior engineers and aligning cross-functional stakeholders.

Builds and operates scalable batch and real-time data pipelines (Databricks/Spark, Kafka), maintains Customer 360 data model and feature pipelines, ensures data quality/governance, delivers governed customer attributes to activation platforms, and supports privacy/compliance.

Build and maintain scalable batch and real-time data pipelines, model data to power analytics and ML, and operate distributed data systems. Collaborate with business, engineering, and data science teams to ensure data quality, documentation, and debugging while adopting new technologies and working in an ambiguous, fast-changing environment.

Build and maintain low-latency, highly available distributed data systems and services to deliver high-quality data for product, analytics, and ML. Partner with data scientists and product teams to design scalable, observable, multi-tenant data products using batch and real-time processing frameworks. Ensure 24x7 reliability, monitoring, and operational excellence while owning end-to-end delivery.

Design, build, and own large-scale batch and streaming data pipelines and core Ads data products. Collaborate with analytics, data science, and engineering teams to model, transform, govern, and deliver high-quality ad datasets for analysis, ML, and reporting while ensuring privacy compliance.

Build and scale robust production data pipelines and automate complex game-related datasets, ensure high data quality, design schemas and data models, collaborate with logging infrastructure and analytics stakeholders to enable analysis across Netflix's games library.

Build scalable, privacy-focused data frameworks and ETL pipelines using big-data technologies (Spark, Presto, Flink, Kafka, Hive). Ensure appropriate handling of personal data, support privacy and legal needs, partner cross-functionally, and lead system design while mentoring teammates.

Support end-to-end data engineering: gather requirements, build and monitor ETL pipelines, implement data quality, contribute to data models, run profiling and basic experiments, document solutions, and communicate progress with stakeholders.

The Senior Data Engineer will design, build, and optimize data pipelines and models, lead a team, and work with stakeholders to deliver data-driven solutions for a Federal client.

Designs, develops, tests, documents, and deploys scalable data architectures, big data platforms, ETL pipelines, and data warehouse solutions. Improves process automation, infrastructure scalability, data reliability, and quality. Troubleshoots complex data issues, performs validation and performance tuning, and collaborates with developers, architects, analysts, data scientists, and functional teams to deliver business-aligned data solutions.

Designs and leads scalable data platforms, event-driven services, orchestration pipelines, and DataOps practices using Python, Airflow, Snowflake, Kafka, and related technologies. The role establishes engineering standards, governance, security, observability, cost efficiency, and AI-assisted development practices. It also mentors engineers, drives cross-functional initiatives, modernizes data ingestion, and ensures reliable, compliant, and well-governed systems.

Leads the design, development, and implementation of scalable data pipelines, APIs, cloud-based integrations, and Customer Data Platform solutions. Builds unified customer data ecosystems supporting analytics, personalization, and enterprise integration. Responsibilities include data modeling, Lakehouse and API optimization, governance, privacy compliance, CI/CD automation, monitoring, troubleshooting, documentation, and cross-functional collaboration. Requires extensive experience with Python, SQL, REST APIs, cloud platforms, data integration tools, CDPs, and modern data engineering architectures.

Owns the end-to-end design and development of a greenfield data and AI platform for federal background investigations. Responsibilities include lakehouse architecture, ingestion and transformation pipelines, Terraform infrastructure, CI/CD, governance, cataloging, lineage, access control, compliance with FedRAMP and NIST requirements, artifact promotion, platform reliability, cost management, and enabling data science and machine learning teams.

Lead database engineering for a federal species-data integration program in a hybrid AWS cloud environment. Responsibilities include harmonizing schemas, documenting source-to-target mappings and lineage, enforcing metadata and data-quality standards, implementing encryption and NIST-aligned security controls, building authoritative source registries, optimizing performance, and leading production migration, validation, monitoring, and operational documentation. The role requires Databricks or equivalent lakehouse expertise, ETL programming, federal governance knowledge, and database migration leadership.

Design, build, and operate large-scale, fault-tolerant real-time and batch data pipelines powering an ad-tech DSP. Collaborate with engineers and data scientists to define data models and SLAs, optimize performance and capacity, enforce data quality and monitoring, mentor engineers, and evaluate new data technologies to scale analytics and ML workloads.

Designs secure Azure data pipelines and analytics foundations using Databricks and ADLS Gen2. Responsibilities include modernizing Oracle workloads, developing medallion data models, implementing data quality and monitoring practices, integrating curated datasets with Power BI, and establishing secure Azure Government identity, network, storage, and access patterns. The role also requires documentation, operational support, stakeholder collaboration, and performance optimization.

Leads data engineering strategy and mentors an analytics engineering team. Designs scalable ELT pipelines, warehouse integrations, data models, API ingestion workflows, and data quality frameworks using cloud and database technologies. Conducts exploratory analysis, supports dashboards and analytical solutions, monitors warehouse performance and costs, and ensures data governance, security, and compliance. Collaborates with data scientists, analysts, engineers, and business stakeholders to deliver healthcare analytics solutions.

Designs and optimizes batch and streaming data pipelines, cloud data warehouses, dimensional data models, and ETL/ELT processes at terabyte scale. Troubleshoots pipeline issues, improves infrastructure reliability and performance, implements data quality and governance practices, and supports privacy compliance. Collaborates with analysts, data scientists, and stakeholders to translate business needs into technical solutions. Leads and mentors data engineers, documents systems, and evaluates emerging technologies.

Own and evolve Stream’s end-to-end revenue operations data platform. Build Python and dltHub ingestion pipelines, SQLMesh transformation models, dimensional data models, quality and observability systems, and GCP infrastructure centered on BigQuery. Integrate GTM systems, enable trusted analytics and reverse ETL, improve reliability and cost efficiency, and establish engineering standards. The role requires hands-on platform ownership in a fast-moving hybrid Boulder startup environment.

Own Mercor’s data platform infrastructure across Snowflake, Fivetran, Airflow, Astronomer, and Hex. Responsibilities include data organization, role-based access control, row-level security, PII masking, governed migrations, pipeline reliability, on-call support, dashboard governance, warehouse cost optimization, and CDC or streaming ingestion. The role also establishes platform standards and builds internal tooling for a new Data Platform team.

Lead Data Engineer responsible for designing, developing, testing, and supporting cloud-based data publication and transformation solutions. Collaborate across Agile teams to build distributed, real-time data pipelines and data warehousing (Redshift/Snowflake), perform unit tests and code reviews, mentor engineers, and adopt emerging technologies to deliver scalable data platforms.

Build and own the unified data layer: ingestion pipelines into ClickHouse, data modeling and entity resolution, canonical metric definitions, quality checks, and internal data tools. Support ad-hoc analyses and work closely with engineering to ensure stable, trustworthy data powering customer dashboards and product BI.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.