Top Data Engineer Jobs

Lead design and deliver cloud-based data engineering solutions across Agile teams. Build, test, and support full-stack data systems using big data, streaming, and cloud technologies; mentor engineers and ensure high-performance, well-tested code.

As a Senior Lead Data Engineer, you'll lead a development team, design and implement data solutions using machine learning, distributed systems, and full-stack technologies, while collaborating with Agile teams and ensuring code quality.

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

Build and optimize high-volume data processing jobs and libraries (Redshift, Athena, Iceberg, Spark, Airflow). Debug and improve query and job performance, and collaborate with data scientists, analysts, and product managers to support analytics and data products.

Lead Data Engineer responsible for designing, building, testing, and supporting scalable data pipelines and cloud-based solutions. Collaborate across Agile teams to optimize data models, ensure data quality and governance, mentor engineers, and apply distributed computing, streaming, and emerging GenAI tooling to deliver personalized marketing and messaging experiences.

Lead Data Engineer responsible for designing, building, and supporting scalable cloud data pipelines and platforms. Collaborate across Agile teams to implement distributed computing, data modeling, quality, governance, and real-time streaming solutions using Python, SQL, Snowflake/Databricks, Spark, Kafka, and cloud services. Mentor engineers, participate in code reviews, and drive technical innovation including GenAI tooling.

Design, implement, and optimize large-scale data processing on Databricks and Spark. Build ETL pipelines, integrate data across AWS services (Redshift, RDS), orchestrate workflows with Airflow, and ensure data warehousing and engineering best practices. Lead and mentor development teams and collaborate with cross-functional stakeholders to drive data strategy and execution.

Work at the intersection of software engineering and operations to investigate production issues, improve reliability, build automation, enhance observability, and support customers. Develop and maintain C# and SQL Server solutions, reduce manual effort, convert production learnings into lasting improvements, and provide occasional after-hours support.

Own end-to-end consumer data domains: ingest, clean, model, and productionize derived attributes and pipelines. Investigate new data sources, ensure quality and provenance, and partner with engineers, data scientists and product teams to deliver trustworthy data products that power applications, agents, APIs and ML models.

Build, maintain, and optimize cloud-based data pipelines and ETL processes. Collaborate with business stakeholders to design data models and deliver scalable warehousing solutions. Implement data quality checks, monitor performance, document systems, and enable self-service analytics (Power BI). Support migration from legacy systems and drive data engineering best practices across environments.

Design and maintain data models, build and optimize data pipelines and database infrastructure, integrate data with BI tools, ensure data quality and governance, troubleshoot performance and downtime, and collaborate with stakeholders to deliver reliable analytics and reporting.

Design, build, and maintain scalable ETL/ELT pipelines and data platforms; parse and process structured and unstructured data; support operations and maintenance for secure information systems; lead cross-functional teams; develop scripts to convert data formats; scale, monitor, and operate data platforms for analytics and reporting.

Maintain and monitor data pipelines that aggregate healthcare claims for the Cost Roll process. Validate and reconcile claim and provider demographic data, troubleshoot data issues, perform data quality checks, and collaborate with stakeholders to ensure reliable downstream financial and clinical systems.

Design and maintain ELT pipelines and Snowflake data models, build dbt transformations, integrate POS and telemetry sources, orchestrate pipelines on AWS, deliver Power BI reports, apply LLM/MCP tooling to automate workflows, and partner with stakeholders to produce production-grade data products and analytics.

Design, build, and maintain ELT pipelines and Snowflake/dbt models; integrate POS and telemetry sources; orchestrate pipelines on AWS; analyze data and produce Power BI dashboards; apply LLM/MCP tooling to automate workflows; collaborate with engineers and business partners to deliver production-grade data products.

Build, run, and maintain end-to-end data pipelines for mass spectrometry and molecular datasets. Ingest, convert, label, and enrich messy scientific data; implement data quality checks, monitoring, tests, and reproducible builds for ML training and product-read datasets.

Design, build, deploy, and support scalable batch and streaming data pipelines and cloud infrastructure on GCP. Develop analytical data products and data warehouse solutions, implement data governance and quality monitoring, automate infrastructure with Terraform and CI/CD, and collaborate with stakeholders to deliver secure, cost-effective finance data solutions.

Contribute to building and maintaining cloud-based data pipelines and data products. Develop, test, and optimize SQL and Python transformations, participate in code reviews, troubleshoot data quality issues, and follow CI/CD and documentation practices to support analytics and AI initiatives.

Design, build, and maintain automated ETL/ELT pipelines to integrate enterprise systems, ensure data quality, and deliver curated business-ready datasets. Develop Python-based data processing, document architectures and workflows, support Palantir and ERP platforms, and collaborate with stakeholders to define requirements and scalable solutions.

Support SOUTHCOM by building and maintaining data ingestion, ETL, integration, and analytics pipelines across WPD and Maven Smart System. Develop, test, and deploy ML/statistical models, ensure data quality/security, document processes, and train stakeholders to produce a Common Intelligence and Operational Picture for mission decision-making.

Build and maintain AEP data pipelines, CTAS queries, and XDM schemas to populate profile-enabled datasets. Monitor identity resolution and profile hygiene, manage CJA connections and data views, troubleshoot AEP dataflows, and document data governance and segmentation practices to support customer experience activation.

Build, optimize, and maintain large-scale data pipelines using IaC; curate massive datasets; enable self-serve AI-driven data access; teach best practices and build ingestion tools to accelerate engineering workflows.

Lead design and build scalable data processing and control-plane solutions on Databricks/AWS. Develop Python/PySpark/SQL pipelines, APIs/SDKs, governance, observability, CI/CD, and operational runbooks. Mentor engineers, drive adoption of AI-assisted development, and ensure data quality, security, lineage, and platform reliability.

Designs, builds, and maintains scalable data infrastructure, streaming and batch pipelines, and data products. Works with stakeholders to model, transform, and deploy data solutions using cloud, streaming, and ETL frameworks while ensuring governance, observability, and automated CI/CD deployments.