Top Data Engineer Jobs in Richmond, VA

Build and architect scalable cloud data platforms, pipelines, and applications for Capital One’s Risk Tech organization. Responsibilities include distributed data engineering, data modeling, warehousing, streaming, security controls, and full-stack solution delivery using Python, Spark, Databricks, Snowflake, and databases. The role influences developers, analysts, and data scientists; collaborates with product and engineering teams; establishes reusable design patterns; communicates technical outcomes; and mentors data professionals.

Design, build, and support scalable cloud data platforms, pipelines, and applications using Python, Spark, SQL, AWS, Databricks, and Snowflake. Develop distributed, real-time, and batch data solutions; apply data modeling, security, encryption, and access controls; enforce reusable engineering patterns; and collaborate with product, software, analytics, and data science teams. The role also involves mentoring, communicating technical concepts to stakeholders, supporting operational resilience, and independently delivering cloud-first data solutions.

Design, build, and support scalable cloud data platforms, pipelines, and applications. The role involves distributed data processing, data modeling, relational and NoSQL databases, streaming workloads, security controls, and data warehousing. It also requires influencing technical teams, mentoring data professionals, establishing engineering standards, collaborating with product and software teams, and communicating technical outcomes to stakeholders.

Build and architect scalable cloud data platforms, pipelines, and applications. Design distributed, real-time, and batch data solutions using Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases. Establish reusable engineering patterns, data security controls, and operational standards. Collaborate with product managers, software engineers, analysts, and data scientists while mentoring team members and communicating technical outcomes to stakeholders.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Develop distributed, real-time, and warehouse data solutions using Python, Spark, SQL, Databricks, Snowflake, and related technologies. Establish data engineering patterns, security controls, observability, and operational standards. Collaborate with product managers, engineers, analysts, and data scientists while mentoring team members and communicating technical concepts to stakeholders.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Lead architectural decisions, establish reusable engineering patterns, evaluate technologies such as Snowflake and Databricks, and deliver distributed, real-time, and warehouse solutions. Collaborate with product, engineering, analytics, and data science teams while mentoring engineers and communicating technical outcomes to stakeholders.

Build and architect scalable cloud data platforms, pipelines, and applications using Python, Spark, SQL, Databricks, Snowflake, and relational or NoSQL databases. Design secure, resilient, and reusable data engineering solutions; support distributed and streaming workloads; enforce engineering standards; collaborate with product, software, analytics, and data science teams; communicate technical concepts; and mentor other engineers.

Build and architect scalable cloud data solutions, pipelines, platforms, and data products. Lead large-scale data initiatives, make architecture and platform decisions, ensure performance and operational efficiency, and apply modern technologies such as Python, Spark, Databricks, and Snowflake. Collaborate with Agile teams, product managers, software engineers, analysts, and data scientists while mentoring engineers and communicating technical concepts to stakeholders.

Designs, develops, tests, and deploys scalable cloud data solutions, lakehouse architectures, and real-time pipelines. Leads data engineering teams, establishes design and quality standards, implements governance and security controls, and partners with product and software engineering teams. The role uses Python, SQL, Spark, Scala, Databricks, Snowflake, Kafka, and cloud platforms to build reliable data products, optimize performance, and support personalized marketing and messaging experiences.

Build and maintain reliable data pipelines, computed tables, and scalable data models using SparkSQL and PySpark. Integrate IoT, product, customer, external, unstructured, and ML-related data into a central data lake for analytics, causal inference, model training, and dashboards. Ensure high data quality and uptime while collaborating with data science, analytics, AI/ML, and engineering teams.

Design, build, deploy, and support scalable cloud data platforms, pipelines, and applications for risk management. Lead technical direction across data engineering, analytics, and machine learning teams; establish reusable engineering patterns; implement security and privacy controls; and collaborate with product managers and software engineers. The role includes mentoring, stakeholder communication, distributed data processing, data modeling, warehousing, streaming, and both relational and NoSQL database solutions.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. The role requires distributed data engineering, data modeling, relational and NoSQL databases, security controls, and cloud data warehousing. Responsibilities include influencing cross-functional technical teams, enforcing engineering patterns, supporting resilient and efficient data solutions, collaborating with product and software engineering teams, communicating technical concepts, mentoring data professionals, and staying current with emerging technologies.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Develop distributed data solutions using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Establish reusable engineering patterns, data security controls, and operational standards. Collaborate with product, engineering, analytics, and data science teams while mentoring others and communicating technical outcomes to stakeholders. Support resilient, high-performance data workloads and contribute to Agile delivery.

Leads large-scale data engineering initiatives for Risk Tech, designing and delivering scalable cloud data platforms, pipelines, and applications. Responsibilities include architecture, data modeling, distributed processing, cloud warehousing, AI-enabled risk solutions, platform evaluation, operational resilience, and technical mentoring. The role collaborates with product managers, software engineers, analysts, and data scientists while independently driving architectural decisions and communicating technical outcomes to stakeholders.

Designs and leads scalable batch and real-time data pipelines supporting AI/ML applications, LLMs, RAG workflows, predictive models, and executive data products. Responsibilities include data architecture, feature stores, governance, security, lineage, performance and cost optimization, and cross-functional technical leadership. The role partners with data scientists, program managers, executives, and engineering teams to deliver reliable enterprise data solutions.

Build and lead scalable cloud data platforms, pipelines, and applications for enterprise risk management. The role involves designing data architecture, developing with Python and Spark, working with relational and NoSQL databases, and evaluating platforms such as Snowflake and Databricks. Responsibilities include delivering large-scale data initiatives, ensuring resilience and operational efficiency, mentoring engineers, enforcing design patterns, and communicating technical concepts to stakeholders. The position requires substantial experience in distributed data, SQL, application development, data modeling, cloud platforms, and pipeline engineering.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Develop distributed, real-time, and warehouse solutions using Python, Spark, SQL, Databricks, Snowflake, and relational or NoSQL databases. Apply data security, encryption, access controls, observability, and reusable engineering patterns. Collaborate with product managers, engineers, analysts, and data scientists while mentoring team members and communicating technical outcomes to stakeholders.

Build and operate scalable cloud data pipelines, platforms, and applications for risk management. Design data architectures, enforce engineering standards, implement security controls, and work with distributed data, AI, microservices, lakehouse systems, and relational and NoSQL databases. Collaborate across Agile teams, mentor data professionals, communicate technical outcomes, and deliver resilient solutions using technologies such as Python, Spark, Databricks, Snowflake, and cloud platforms.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Develop distributed and streaming data workloads, data models, and relational and NoSQL solutions using Python, Spark, SQL, AWS, Databricks, and Snowflake. Establish reusable engineering patterns, security controls, and operational standards while collaborating with engineers, product managers, analysts, and data scientists. Mentor colleagues, communicate technical outcomes, and deliver resilient data products with minimal supervision.

Build and architect scalable cloud data platforms, pipelines, and applications for Capital One’s Risk Tech organization. Develop distributed data solutions using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Design secure, resilient, and operationally efficient data systems, including streaming and AI-enabled solutions. Collaborate with Agile teams, product managers, engineers, analysts, and data scientists while mentoring peers and communicating technical concepts to stakeholders.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Designs, builds, and maintains secure data pipelines, ETL processes, data warehouses, and quality controls. Collects, validates, cleanses, and analyzes data; develops SQL queries, Tableau dashboards, and AWS-based solutions; investigates data integrity issues; documents standards; and partners with stakeholders to deliver technical solutions using Agile and DevOps practices.

Leads enterprise data engineering strategy and architecture for pipelines, external data sharing, and scalable data platforms. Designs and codes critical data solutions while driving adoption of modern technologies, governance, resiliency, operational excellence, and unified data architecture. Advises stakeholders, mentors engineers, recruits talent, evaluates emerging data and AI technologies, and develops platform roadmaps. The role requires hands-on expertise across AWS, lakehouse architecture, streaming, distributed computing, data warehousing, and multiple programming languages.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.