Top Senior Data Engineer Jobs

Lead end-to-end data engineering on GCP: design scalable data platforms and architectures (lake/lakehouse/warehouse), build and optimize BigQuery/Spark pipelines, ensure data quality/security, enable ML workflows with Data Science, mentor engineers, and own delivery and performance.

Design, build, and optimize scalable AWS-based data pipelines, data lakes, and warehouses. Migrate legacy systems, enforce data quality/security/governance, implement ETL/ELT and big-data solutions, adopt CI/CD and IaC, collaborate with stakeholders, and provide technical leadership and troubleshooting.

Design, build, modernize, and maintain operational and analytical data capabilities for Wealthscape Reporting, Analytics and Insights. Perform solution design, data analysis, ETL development, and production rollouts using Snowflake, AWS, cloud databases, and CI/CD pipelines. Collaborate across teams in a fast-paced financial services environment.

Design, build, and maintain ETL/ELT data pipelines and orchestration using Airflow, Python, and PySpark. Develop custom connectors, implement DataOps and CI/CD for data workflows, monitor and troubleshoot pipelines, validate data quality, and document processes while collaborating with cross-functional teams and supporting large-scale distributed data systems.

Design, build, and maintain scalable data pipelines on AWS using EMR, Spark, and PySpark. Optimize EMR clusters and Spark jobs, implement ETL/ELT processes, ensure data quality, automate ingestion, and collaborate with data scientists and analysts. Document infrastructure, participate in code reviews, and monitor performance and costs.

Design and deliver cloud-based data architectures, ETL/ELT pipelines, and data lake/warehouse solutions for a Department of War client. Lead a small engineering team, implement migration strategy, build live-data dashboards, recommend tools, and coordinate with government PM. Hybrid role at Fort Bragg with quarterly travel; requires active Secret clearance and US citizenship.

Lead design and implementation of Snowflake-centered cloud data platform and scalable ETL/ELT pipelines. Build data models, ensure governance/HIPAA compliance, optimize performance and costs, create Power BI reports, mentor engineers, migrate legacy systems, and enable analytics and AI/ML initiatives through robust data integration, testing, CI/CD, and security practices.

Design, build, and maintain scalable ELT pipelines and data models for insurance (Guidewire) domains and Generative AI. Ensure data quality, performance, governance, and security; support analytics, AI model training (embeddings, vector DBs), dashboards, testing, and mentoring.

Design, build, and maintain scalable data pipelines and architectures. Perform data extraction, cleansing, transformation, and ensure data quality and accessibility. Collaborate with data scientists, analysts, and product teams to deliver data solutions and troubleshoot data issues.

Design, build, and maintain scalable data pipelines, lakes, and infrastructure using Databricks, Spark, Python, and AWS. Collaborate with ML engineers, data scientists, and analysts to deliver data products, ensure data quality and governance, implement DevOps/SDLC practices, mentor junior engineers, and evaluate new data technologies.

The Data Integration Lead designs and manages data ingestion pipelines, ensuring data integrity and security while supporting ETL processes in classified environments.

The Data Engineer will develop data-driven decision analysis products, provide analytic consulting, support stakeholders with tools, and optimize defense systems for national security.

Design, build, and maintain scalable data lakes, warehouses, and production ETL/ELT pipelines. Automate data pipelines, ensure data quality via testing, collaborate with architects, data scientists, and DevOps, integrate data solutions into client environments, and implement data recovery and security measures.

Build, operate, and optimize large-scale data pipelines and warehouse schemas. Own Airflow orchestration, AWS-based data security (Lake Formation), automated data quality, and production incident resolution. Partner with stakeholders, mentor engineers, and drive DataOps best practices for performance, cost efficiency, and governance.

Design, build, and maintain Python data pipelines and business data objects. Implement source-to-target mappings, CI/CD pipelines, unit and integration tests, and deploy solutions. Apply software design and architecture best practices, mentor team members, and resolve complex technical problems across functional teams.

The Staff Data Engineer will design and build data pipelines, reports, and tooling for the Concepts Lab, ensuring reliable AI systems and leading data modeling and analytics efforts. The role requires extensive experience in production data systems and collaboration across teams to transition concepts into production.

Lead and mentor a data engineering team to design, build, test, and support cloud-based data solutions. Develop distributed, real-time and batch data pipelines using Java/Scala/Python, NoSQL and RDBMS, and cloud data warehousing (Redshift/Snowflake). Collaborate with product managers and Agile teams to deliver performant, production-ready systems.

Build and operate scalable data pipelines and services to power internal products. Design data models, curate high-quality datasets, collaborate with cross-functional teams, and mentor engineers while improving platform reliability, observability, and AI-ready data layers.

Lead and administer cloud data warehouse platforms (e.g., Snowflake), design scalable data architectures and models, build and maintain ETL/ELT pipelines, implement CI/CD and version control, optimize performance and costs, migrate/load data from APIs/SaaS/ERP, and ensure security, governance, and testing best practices across the data lifecycle.

Design, build, and optimize Snowflake-based data warehouses and dbt transformation pipelines. Develop scalable ETL/ELT ingestion, tune SQL and warehouse performance, implement data quality/testing (dbt tests), and maintain CI/CD and version-controlled data workflows while collaborating with analysts, data scientists, and stakeholders.

Lead graph data engineering to design, implement, and scale Neo4j-based graph platforms. Build graph models, ingestion pipelines (batch and streaming), integrate graphs with analytics/ML, establish governance, and enable graph-driven business use cases like fraud detection and Customer 360.

Lead design, build, and operate real-time, Flink-based streaming data platforms. Develop low-latency, stateful pipelines, enforce event schemas/contracts, ensure fault tolerance and observability, mentor engineers, and partner with architecture and compliance teams to deliver resilient streaming solutions for banking use cases.

Design and implement scalable data engineering, ETL, data lake and reporting solutions. Build data infrastructure and pipelines using Oracle, Snowflake, Airflow, Informatica and AWS. Collaborate with product owners and analysts, ensure alignment with enterprise data strategy, perform data modeling, reconciliation, automation (Control‑M), and coach team members while participating in SDLC and DevSecOps practices.

Design, develop, test, deploy, and maintain scalable ETL and BI/data warehouse solutions in cloud/on-prem environments. Build CI/CD pipelines, automation, test frameworks, and data quality processes. Collaborate with analysts and engineers in Agile teams to deliver reliable, observable, enterprise-grade data platforms.

Design, build, and operate enterprise-scale data pipelines and platforms (ETL/ELT) using Palantir Foundry and Databricks. Parse and process structured and unstructured data, develop scripts to convert data, mentor cross-functional teams, and monitor/scale data solutions for mission-driven analytics.