Top Senior Data Engineer Jobs in Richmond, VA

Designs, builds, and operates scalable enterprise data platforms supporting ingestion, processing, storage, governance, analytics, and AI/ML. Responsibilities include developing batch and real-time pipelines, lakehouse and warehouse architectures, orchestration, infrastructure automation, CI/CD, monitoring, security, cost optimization, and observability. The role partners with data, ML, DevOps, security, and application teams, troubleshoots complex platform issues, establishes reusable engineering standards, contributes to architecture strategy, and mentors engineers.

Own end-to-end data platform architecture across ingestion, transformation, modeling, and serving. Improve warehouse efficiency, access control, reliability, observability, orchestration, CI/CD, and developer experience. Lead cross-functional technical initiatives, build monitoring and alerting, mentor engineers, guide architectural decisions, and develop AI-powered data tooling. The role requires strong SQL and Python skills, production experience with Airflow, BigQuery, and containerized infrastructure, plus architectural ownership and stakeholder communication.

Design, build, maintain, and optimize Spark and Scala ETL/ELT pipelines on AWS for terabyte- and petabyte-scale datasets. Work across S3, Snowflake, Kafka, and monitoring systems, resolve production data-quality and performance issues, write automated tests, participate in architecture and code reviews, and support on-call operations. Use generative AI to improve pipeline development, monitoring, and engineering productivity.

Lead architecture and hands-on engineering to design, scale, and govern a cloud-native lakehouse using Databricks and Microsoft Fabric. Build batch/streaming pipelines, ensure HIPAA-compliant PHI handling, produce analytics-ready datasets, mentor engineers, and drive platform strategy, governance, and reliability.

Design and implement end-to-end data solutions on Microsoft Fabric and Azure for insurance datasets. Build ingestion pipelines (batch, streaming, API), Lakehouse architectures, and data models. Enforce governance, security, compliance, and cost/performance optimizations while guiding engineers and stakeholders on architectural best practices.

Develop and maintain large-scale, cloud-native data services and distributed pipelines for multi-tenant healthcare systems. Responsibilities include data modeling, database component development, SQL programming, performance tuning, testing, deployment, code reviews, proof-of-concepts, and full software development lifecycle ownership. Collaborate with global engineering teams, evaluate technologies, apply security and operational requirements, and use AI tools to accelerate development.

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.

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.

The job involves building responsive web dashboards, advanced data visualizations, and analytics tools for unmanned aircraft systems, requiring a deep understanding of UI/UX design and data presentation.

Lead the data engineering strategy and architecture for distributed pharmaceutical data systems. Design and scale pipelines, APIs, and data platforms while ensuring reliability, governance, quality, and PII/PHI protection. Drive technical decisions, improve developer efficiency, deliver business impact, influence cross-functional roadmaps, mentor engineers, communicate with executives, and promote organizational adoption of new technologies and processes.

Develop business intelligence and analytics solutions using financial, operational, RF, and video performance data. Build scalable data models, dashboards, reports, automation workflows, and ETL/ELT processes using Power BI, Tableau, DataMiner, and Qligent. Analyze trends and anomalies, monitor satellite and video delivery performance, translate business requirements into actionable insights, and collaborate with enterprise stakeholders in a fully remote consulting role.

Build and maintain ETL and data pipelines using Python, PySpark, and AWS services. Orchestrate workflows, develop event-driven integrations, optimize data storage and queries, process API and JSON data, and implement data quality monitoring. Support CI/CD, production operations, cloud migration, data lake and lakehouse initiatives, and near-real-time systems while collaborating with stakeholders to deliver technical solutions.

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.

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.

Lead architecture, systems, and roadmap for Coinbase's Compliance Reporting Data Platform. Design and build production-grade data models, pipelines, quality frameworks, validations, and certification processes. Establish data contracts, monitoring, reconciliation, and audit-ready processes. Partner with engineering and compliance stakeholders, respond to regulatory exams, and mentor engineers while shaping standards and documentation.

The Data Engineer is responsible for designing data workflows and pipelines, implementing systems engineering principles, and deploying data streaming technologies while ensuring compliance with industry regulations and best practices.

Lead and mentor multiple Databricks-focused data engineering teams, define engineering best practices and reusable frameworks, drive CI/CD and performance/cost optimization, build and support analytical and streaming pipelines, and collaborate with architects, product owners, and cross-functional partners to deliver high-quality data products.

Design, develop, maintain, and optimize ADABAS database structures and data solutions. Support mainframe data processing (Natural, COBOL, JCL), perform migrations to modern platforms, tune performance, ensure data integrity/security, troubleshoot issues, and collaborate with developers, architects, and analysts on modernization, API/cloud integration, and data warehousing initiatives.

Leads the design and delivery of scalable data pipelines, dbt models, orchestration workflows, and analytical data models. Establishes data architecture, engineering standards, quality frameworks, governance, observability, and self-service analytics practices. Partners with stakeholders, leads technical reviews, mentors engineers, supports hiring, oversees analyses, and participates in incident response and continuous improvement.

Design, build, and maintain data lakes, data warehouses, and end-to-end data pipelines for a multi-tenant SaaS platform. Lead code and design reviews, write infrastructure-as-code, optimize performance and scalability, participate in data governance, mentor junior engineers, evaluate new technologies, automate tests, collaborate cross-functionally, and join on-call rotation to ensure reliable data delivery.

Own end-to-end data engineering for a business domain, including API and other-source ingestion, dbt models, orchestration, data-quality testing, monitoring, documentation, and performance. Build reliable, AI-ready single sources of truth and ensure data freshness, accuracy, traceability, and scalability. Collaborate with stakeholders and the Director of Data, establish engineering patterns, review work, and mentor other engineers.

Designs and maintains scalable data pipelines and platforms, develops ETL/ELT processes, and migrates heterogeneous external data into standardized enterprise models. The role includes data ingestion, transformation, quality validation, reconciliation, and billing-data standardization across systems. It collaborates with engineering, product, DevOps, and DBA teams, contributes to technical decisions, mentors engineers, and applies AI-assisted development tools. The position is based in Richmond, Virginia, with four days in office and one day remote.

The Senior Data Engineer will design and implement lakehouse architectures, optimize ClickHouse deployments, develop ETL/ELT pipelines, and create data models to support analytical and operational functions.

Lead the design, development, and optimization of scalable batch and real-time data pipelines and platforms. Build cloud-based data solutions using distributed processing, streaming technologies, and modern data warehouses. Define data models, improve performance and reliability, establish engineering best practices, and provide technical guidance to data engineers while collaborating with architects, product teams, developers, and stakeholders.

Design, build, and own business-critical data infrastructure, including AWS Glue and Spark pipelines, cloud warehouse or lakehouse systems, orchestration, and Tableau BI platforms. Define event-driven contracts, data models, schemas, observability, and batch or streaming architectures across multi-tenant, multi-region environments. Collaborate across producer and consumer teams while owning production data infrastructure and outcomes end to end.