Top Data Engineer Jobs

Leads architecture, scaling, governance, and implementation of enterprise data platforms across multi-cloud and air-gapped environments. Designs batch and streaming systems, optimizes Spark and Trino/Presto clusters, implements Terraform-based infrastructure, establishes data quality, lineage, security, and compliance controls, and leads Agile engineering pods while mentoring staff. Requires advanced expertise in distributed data systems, cloud platforms, Kubernetes, programming, and regulated defense environments.

Design, build, and operate scalable real-time and batch data pipelines ingesting high-frequency telemetry, simulation, and trackside data. Own Kafka/Flink streaming components, Databricks lakehouse implementations, and infrastructure-as-code deployments. Develop software in Python/Java/SQL, integrate systems, automate testing and releases, performance-tune pipelines, and mentor peers to ensure resilient, secure, high-performance data delivery for motorsports teams.

Designs and deploys end-to-end data infrastructure, including schemas, warehouses, automated pipelines, data quality monitoring, and curated datasets. Builds Power BI solutions for self-service analytics and partners with product, marketing, sales, data science, and machine learning teams. Applies analytical techniques, productionizes machine learning and LLM use cases, and translates complex data into business insights.

Designs, builds, and maintains scalable data pipelines and integration solutions using Kafka, Elastic Stack, AWS, and OpenShift. Integrates network operations data, develops ETL/ELT processes, data lakes, warehouses, dashboards, and CI/CD automation. Optimizes ingestion, query, indexing, storage, and cluster performance while ensuring data quality, security, and DoD compliance. Collaborates with analysts, visualization engineers, end users, and support teams to deliver documented, actionable data solutions.

Design, build, and own scalable ETL/ELT pipelines, data models, and data infrastructure. Improve data quality, observability, performance, and CI/CD for data workflows. Partner with analytics, ML, software, and product teams, mentor engineers, and resolve complex data issues.

Design, build, and maintain scalable batch and real-time data pipelines and data lake architecture. Improve observability and SLOs, optimize ETL/ELT, develop dbt workflows, support event-driven architectures, integrate financial APIs, and collaborate with analytics and ML teams to deliver reliable, model-ready data products.

Designs and deploys scalable data pipelines, preprocessing workflows, feature-engineering services, and machine learning models for secure Department of Defense environments. Develops reproducible, tested, containerized data and ML services using Python, Spark, Databricks, Docker, Terraform, SageMaker, and MLflow. Performs exploratory analysis, visualization, data storytelling, model evaluation, and decision-support development while collaborating with product, software, platform, DevSecOps, data science, and mission teams.

Participate in an internship focused on database modeling and optimization, data analysis and consolidation, data quality, and data support. Work alongside experienced professionals on real-world projects while developing technical and professional skills. The program requires prior knowledge of data concepts, spreadsheets, databases, programming logic, and analytical problem-solving.

Senior Data Engineer responsible for designing, building, and optimizing enterprise data pipelines, architectures, and scalable reporting solutions. The role integrates data from diverse business platforms, develops Snowflake and dbt solutions, implements governance and security, and enables self-service analytics. It partners with executives, product teams, analysts, developers, and data scientists while mentoring team members, evaluating emerging technologies, and delivering reliable data assets and actionable insights.

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.

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.

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.

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.

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.

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.