Top Data Engineer Jobs

Designs and maintains ETL pipelines, data architectures, databases, data models, and master data management solutions. Analyzes, profiles, cleans, and validates complex datasets; establishes data quality standards; and develops automated data management processes. Partners with architects, data stewards, and business stakeholders to improve data integrity and support business requirements. Communicates insights through reports and presentations while using cloud, database, streaming, CI/CD, and data modeling technologies.

Design and optimize the enterprise data warehouse and ELT pipelines (batch and real‑time). Integrate Databricks, CRM, and external systems into a governed, observable data platform. Drive automation, cost optimization, and monitoring; mentor junior engineers; and partner with Analytics, Ticketing, and Marketing to deliver scalable data solutions that support reporting and ML initiatives.

Build and maintain data warehouses, pipelines, scalable data models, and data quality frameworks for Waymo commercialization. Translate business requirements into database models, transform raw data for reporting and insights, ensure PII and ACL compliance, document solutions, and collaborate with data producers and consumers. The role requires SQL expertise, database knowledge, and programming experience in Python, C++, or Java, with preferred experience in distributed data processing and cross-functional project leadership.

Designs and delivers a global analytics platform supporting reporting, visualizations, self-service analytics, and embedded data products. Responsibilities include data collection and processing, semantic modeling, warehouse and pipeline development, query optimization, governance, analytics enablement, and stakeholder collaboration. The role provides actionable insights from complex travel data and builds integrated analytics applications using cloud platforms and business intelligence tools.

Designs, builds, tests, and maintains software and data infrastructure for business-facing solutions. Develops CI/CD and DevOps practices, supports applications throughout their lifecycle, contributes to internal tools and standards, and collaborates directly with end users. Requires Python, SQL, large-scale data engineering experience, Azure and Databricks knowledge, and familiarity with Power BI, Copilot Studio, and AI tools.

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.

Designs and maintains Snowflake-based cloud data pipelines and lake architectures for asset-based finance and mortgage servicing analytics. Leads legacy-system migrations, ETL/ELT development, data validation, reconciliation, and financial integrity controls. Translates business requirements into Jira epics and user stories, supports project planning, testing, cutover, and go-live readiness, and coordinates with business, finance, operations, and technical stakeholders.

Own Doctronic’s end-to-end data platform, including CDC pipelines from production databases into S3, Iceberg, and Snowflake; dbt transformations; orchestration; warehouse architecture; monitoring; and governed access to PHI. Establish trusted, documented data layers for analytics, finance, dashboards, and AI model training. Implement HIPAA-compliant access controls, anonymization, and deletion workflows while partnering autonomously with product, finance, marketing, partnerships, and AI teams.

Leads enterprise data architecture and modeling, including logical and physical schemas, cross-domain integrations, data dictionaries, lineage, metadata standards, and complex schema reviews. Guides scalable architecture planning, estimation, budgeting, and model enhancement while supporting teams and staying current with emerging technologies. Requires expertise in dimensional modeling, data vault, lakehouse architectures, SQL, transformation tools, cloud warehouses, data lakes, and cross-functional agile collaboration.

Build and operate scalable Databricks Lakehouse data platforms, including Bronze/Silver/Gold pipelines, feature tables, batch and streaming ingestion, data quality controls, modeling, performance optimization, governance, and production support. Partner with data science, AI, and platform engineering teams to deliver trusted healthcare and member data products for machine learning and decision intelligence workloads.

Lead the architecture, delivery, governance, quality, and reliability of Databricks Lakehouse data products. Build scalable batch and streaming pipelines, feature layers, and real-time data architectures supporting machine learning and decision intelligence. Optimize Spark and Delta Lake workloads, enforce PHI/HIPAA compliance, and establish observability and data quality standards. Lead a small engineering team while remaining hands-on, partnering with data science, AI, platform, and backend teams on microservices, databases, rules engines, APIs, testing, and resilient production systems.

Designs, builds, and maintains scalable AWS data pipelines, integrations, lakehouse infrastructure, and Airflow workflows. Processes commercial pharmaceutical data, supports pharma KPIs and analytics, and develops pipelines for AI, machine learning, generative AI, LLM, embeddings, and RAG applications. The role also includes legacy-system migration, pipeline monitoring and optimization, data quality assurance, and translating pharmaceutical business needs into reliable data solutions.

Develop and maintain Python-based automation tools, scripts, and data pipelines for complex datasets. Perform exploratory data analysis, apply statistical methods, troubleshoot data and code issues, and translate analyst workflows into scalable solutions. Collaborate with cross-functional engineering and data teams using Git and Agile practices, document tools, communicate findings, and support mission-focused aerospace and defense programs.

Design and implement end-to-end AI features, scalable data pipelines, ETL/ELT workflows, Generative AI and agentic systems, ML and BI solutions, and MLOps/LLMOps practices across AWS, Azure, and GCP. Apply RAG, embeddings, vector databases, security governance, evaluation, reliability, and guardrails. Own technical workstreams, mentor junior engineers, and promote software engineering best practices in consulting environments.

Build and maintain Python ETL pipelines for large geospatial and energy datasets. Design and optimize PostgreSQL/PostGIS schemas, SQL queries, spatial workflows, validation controls, and data-quality processes. Maintain Linux-based processing environments and automation, diagnose pipeline and database issues, and collaborate with product, GIS, and data science teams to deliver reliable data products.

Build and maintain scalable infrastructure and data platforms supporting risk analytics, regulatory reporting, and risk measurement processes such as FRTB and VaR. Develop software, data transformation pipelines, workflow automation, testing, deployment, monitoring, and security controls. Provide technical leadership, collaborate with Risk Managers, Quants, and Front Office teams, and guide strategic engineering decisions across enterprise risk platforms.

Design and scale Zip’s enterprise data and AI platform across batch, streaming, structured, and unstructured workloads. Build reliable pipelines, AI-ready data products, semantic models, vector and hybrid search, and RAG capabilities. Establish standards for modeling, testing, governance, security, observability, and automation. Partner with engineering, data science, product, and business teams while mentoring engineers and influencing platform architecture.

Design, build, and maintain scalable, high-performance data pipelines, ingestion, transformation, and data export solutions. Implement data quality, testing, and monitoring, collaborate with product and stakeholders to build analytical data models, support production jobs, perform PoCs, and contribute to roadmap, standards, and documentation.

Own and scale automated patient-data ingestion and transformation pipelines from EMR systems into clinical-trial matching workflows. Design reliable asynchronous, queue-based, containerized architectures; manage schemas, validation, data quality, monitoring, and production issues. Partner with AI and product engineering teams on healthcare data foundations, APIs, scalability, and trustworthy clinician-facing workflows. Operate autonomously in a startup environment while applying strong systems thinking and pragmatic technical judgment.

Designs and leads scalable real-time and batch data pipelines, data platforms, architectures, data products, and intelligent APIs. Ensures data quality, supports BI and AI/ML initiatives, and develops ingestion, processing, migration, and analytical solutions for big data, NoSQL, and data warehouse environments. Provides technical leadership, mentorship, code reviews, testing, CI/CD implementation, system monitoring, troubleshooting, and cross-functional translation of business requirements into reliable cloud-based solutions.

Design and build modern GCP lakehouse architectures, including Python and Spark pipelines, BigQuery ingestion, Kafka CDC, Delta Lake and Iceberg UniForm integrations, Delta Sharing endpoints, Snowflake and Databricks connectivity, data lineage, governed access controls, and semantic-layer alignment. Collaborate cross-functionally, troubleshoot full-stack issues, and deliver reusable data-sharing adapters and end-to-end data features.

Support SAP BRIM data analysis, migration, and development across SOM, Convergent Charging, Convergent Invoicing, and FI-CA. Design SAP BODS ETL workflows, configure EMIGALL migration objects, develop mappings and reconciliation reports, validate migrated data, troubleshoot issues, and support testing, cutover, production stabilization, documentation, governance, and stakeholder collaboration.

Build and lead complex data integration projects from client onboarding through ongoing platform usage. Analyze and resolve data discrepancies, troubleshoot SIS and SSO integrations, develop internal Retool applications, perform code reviews, prioritize backlogs, manage projects, and mentor teammates. The role requires advanced SQL, development expertise, technical leadership, and collaboration with internal stakeholders, with Ruby on Rails, AWS, Git, and K-12 education experience preferred or desired.

Designs, develops, and optimizes enterprise data platforms, ETL workflows, databases, reporting solutions, and dashboards. Leads technical designs, SDLC processes, troubleshooting, documentation, and production support while collaborating with stakeholders and vendors. Uses SQL Server, SSIS, T-SQL, Microsoft BI tools, and DevOps technologies to build scalable, reliable data integration and business intelligence solutions. Supports cloud migration and may use Azure, Fabric, Snowflake, or Databricks.

Build and maintain scalable data infrastructure, pipelines, warehouses, and models on cloud platforms, primarily GCP. Develop batch and real-time ETL/ELT workflows using Python, SQL, and Airflow-based tools; implement data quality, governance, metadata, lineage, CI/CD, and automation. Support production machine learning environments, optimize pipeline performance, collaborate with architects and analytics teams, lead solution delivery, and mentor data engineers.