Top Senior Data Engineer Jobs

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.

Lead architecture and implementation of a large-scale analytics stack: design foundational pipelines, establish modeling/orchestration standards, integrate AI-assisted engineering tools, translate business questions into version-controlled data models, partner with stakeholders to define metrics and data contracts, migrate logic into maintainable pipelines, and optimize cloud warehouse performance for scale.

Design, harden, and scale Snowflake and AWS data platforms, build dimensional data models and resilient Airflow pipelines, optimize complex SQL, develop dbt semantic layers, deploy ML models in AWS, integrate AI/LLMs into workflows, and build data APIs (FastAPI/Flask) to enable enterprise analytics and operational ROI.

Design and implement data cleaning, preprocessing, integration, and quality checks. Develop dashboards and complex visualizations (Tableau/Power BI). Apply AI/ML techniques for unstructured data analysis and provide data engineering support for visualization and analytics.

Lead and mentor a team of data engineers to design, develop, test, implement, and support cloud-based data platforms and full-stack solutions. Build distributed, real-time, and batch data systems using Java/Scala/Python, big data tools (Spark, Hadoop, Kafka), NoSQL and RDBMS, and data warehouses (Redshift, Snowflake). Collaborate with Agile teams, perform unit tests and code reviews, and drive technology experimentation and transformation.

Design, build, and deploy scalable data pipelines and validation for government production environments. Establish and maintain schemas, optimize SQL, handle messy/disparate data sources, work with large (100GB+) workflows, use Databricks/Spark/Python, and support ML/predictive solutions while mentoring teammates and engaging with clients. Active Secret Clearance required.

Lead data engineering efforts, manage data governance, ensure compliance, and oversee data platform economics while designing scalable systems.

Design, build, and maintain scalable data pipelines and models for Finance and operations; collaborate with analysts and data scientists to provision trusted analytics; ensure governance, performance, testing, and incident root-cause fixes; recommend technologies and mentor team members to adopt cloud-first, best-practice data architectures.

Support the creation of business intelligence tools for the War Research Enterprise by developing scalable data pipelines and governance policies while collaborating with cross-functional teams.

The Data Engineer role involves developing and maintaining data pipelines for forecasting in commercial real estate, ensuring data integrity and supporting analytics teams.

The Scientific Data Engineer will design data architecture, build ETL/ELT pipelines, implement data quality checks, and develop semantic layers for AI-consumable data products in healthcare research.

Design, develop, and maintain data pipelines and software solutions to process and combine diverse data sources. Perform statistical analysis (regression, anomaly detection, clustering), automate and optimize data products, communicate findings to technical and non-technical stakeholders, elicit system requirements, validate performance, and support time-sensitive intelligence operations.

Lead implementation of ETL patterns and reliable data infrastructure, partner with business and engineering teams to prioritize and deliver integrations, APIs, and reusable components, and support operational data applications in an Agile environment.

Build, deploy, and operate Azure-based data integration and analytics solutions using Databricks, Delta Lake, ADF, and Synapse. Implement ingestion, transformation, data models, governance, performance optimization, CI/CD, and MLOps patterns while collaborating with architects, offshore teams, and business stakeholders.

The Data & AI Engineer III will lead cloud data engineering solutions, develop data infrastructure, establish architectures, mentor team members, and secure data around cloud systems.

The Data Engineer will develop data pipelines, manage complex data sets, and ensure data reliability while collaborating within an Agile/Scrum team.

The Data Engineer develops data pipelines, supports Agile teams, ensures data performance, advocates for lean practices, and collaborates across functions to deliver insights for federal customers.

Responsible for developing data pipelines, ensuring data extraction performance, and collaborating in an Agile team to provide data-driven solutions.

This role involves developing data pipelines, ensuring data performance, automating processes, and collaborating within Agile teams to meet complex data requirements.

As a Sr Data Engineer, you'll conduct feasibility analyses, develop applications, monitor processes, consult with users, and ensure compliance while serving as a mentor to junior analysts.

Design, develop, and maintain scalable ETL pipelines using Ab Initio to transform upstream data for Oracle Financials SaaS. Perform data profiling, quality assessments, complex SQL development, Unix/Linux scripting, metadata management in EME, deployment support, performance tuning, and collaborate with architects and analysts. Provide operational support and mentor junior analysts while ensuring compliance and risk controls.

Design, implement, and maintain scalable data infrastructure and real-time processing for analytics platforms. Migrate systems from AWS to on-prem Kafka/Spark, implement data transformations, support production, optimize pipelines, query and analyze data, and build scripts and APIs to operationalize data science work.

Own and build the end-to-end data layer: extraction pipelines from messy supplier emails and PDFs, the unified data model, reliable observable pipelines, and ML-driven extraction evaluation. Drive extraction accuracy, data quality, lineage, monitoring, and automation to scale coverage and enable engineering and product to depend on the data layer.

Own and scale ReadyOn's data platform: design/build real-time and batch pipelines, implement ingestion and transformation workflows, model schemas in Snowflake and PostgreSQL, ensure observability and data quality, build connectors and feature-store roadmap, and provide technical leadership and mentorship across data, AI, and backend teams.