Top Data Engineer Jobs

The Data Engineer will lead data lifecycle strategy, manage data acquisition and cleansing, and collaborate with teams to optimize data solutions and architecture.

Design and build production-scale data pipelines, orchestration, and integrations on GCP to enable metadata-driven governance, lineage, automation, and AI-enabled workflows. Implement secure, observable, cost-efficient cloud data solutions, embed governance into engineering workflows, and drive CI/CD, testing, monitoring, and platform engineering best practices.

Design, build, and maintain scalable ETL/ELT pipelines (batch and streaming); manage data catalogs and API integrations with OAuth2; collaborate with cross-functional teams to ensure data quality and platform reliability for mission-driven projects.

Design and build scalable backend services and data ingestion pipelines, perform data modeling and large-file processing, develop reusable APIs, and optimize systems for speed and scalability while collaborating in an Agile team.

Design, build, and maintain core data infrastructure and automated end-to-end pipelines to ingest, process, store, and transform data. Develop data models and schemas, implement processing frameworks, enforce data quality and lineage, troubleshoot pipelines, collaborate with data scientists and security teams, document architecture, and support data governance and continuous improvement.

Builds and manages enterprise data pipelines, transformations, reporting structures, and analytics infrastructure using operational data from Oracle Fusion, SAP, and related platforms. Supports secure, accurate financial, HR, and procurement reporting through Microsoft Fabric and Power BI, including real-time dashboards and historical analysis.

Migrate SQL Server data products and analytical workloads to a modern cloud platform. Redesign legacy SQL, ETL processes, and business logic; build scalable pipelines and analytical models; validate data quality; collaborate with stakeholders; and improve platform reliability, performance, cost efficiency, and AI-assisted data engineering automation.

Lead architecture and delivery of scalable data infrastructure: design and optimize ETL/ELT pipelines, enterprise data models, and data quality frameworks. Define platform standards, mentor engineers, drive cross-functional initiatives, and partner with analytics, data science, and engineering stakeholders to ensure reliable, production-grade data assets.

Designs, develops, and optimizes enterprise data pipelines and integrations into Snowflake from Oracle Fusion, databases, and cloud applications. Ensures data quality, governance, security, scalability, and performance while supporting Power BI, AI, machine learning, and self-service analytics. Partners with business and technical stakeholders, leads engineering standards, mentors junior engineers, manages source-system migrations, and evaluates emerging data technologies.

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.

Design and support ETL, data migration, transformation, import, export, and validation workflows for moving legacy EHR data into athenahealth systems. Build Python and SQL utilities, define source-to-target mappings, support HL7 and FHIR interoperability, validate EHI exports, investigate data quality issues, document processes, and collaborate with product, implementation, operations, sales, and engineering teams in a remote environment.

As a Data Engineer, you will design and implement data pipelines, analyze requirements, and develop software solutions to drive operational efficiency.

Design, develop, and optimize data warehousing and ETL/ELT solutions on Microsoft SQL Server and cloud platforms. Perform data profiling, cleansing, dimensional modeling (Kimball), and performance tuning. Build semantic models and Power BI/Microsoft Fabric solutions, advise on architecture, mentor teammates, assist in pre-sales/scoping, and promote BI best practices across clients.

Design and build large-scale ETL/ELT pipelines and analytical architectures on GCP. Assess, plan, and drive enterprise data and cloud migration programs, modernize legacy systems, optimize query performance, and enable scalable cloud-native solutions for clients.

Design, build, and operate scalable PostgreSQL databases and cloud-native data infrastructure. Build and maintain ETL/ELT pipelines, optimize query and server performance, ensure high availability and disaster recovery, improve observability, troubleshoot production issues, guide schema and application design, evaluate database technologies, and mentor junior engineers to support reliable, secure data-driven applications.

Design, build, and operate scalable batch and near-real-time data pipelines on AWS. Lead complex data engineering projects, translate business needs into data models and technical solutions, ensure data quality and reliability, and shape platform roadmaps. Integrate AI workflows such as LLM orchestration, embeddings, and vector search while collaborating with product and engineering teams across financial and analytical products.

Designs, builds, operates, and modernizes enterprise data pipelines, integrations, and Microsoft Fabric platform capabilities. Develops Python, PySpark, SQL, and Delta Lake solutions; supports ERP, CRM, dealer, manufacturing, and SaaS integrations; and maintains legacy ETL, stored procedures, middleware, and linked-server processes. Ensures reliability through testing, validation, monitoring, reconciliation, incident response, documentation, and CI/CD practices. Collaborates across technical and business teams while providing technical guidance and supporting critical production issues.

Build and manage high-volume analytics and production ETL/ELT systems, ensuring reliable data ingestion, quality, observability, security, compliance, and scalable performance. Partner with Data Science, Engineering, and Product teams to support accurate Search data logging and usage. Define data-system SLAs, optimize cost and performance, and support massive datasets, experimentation, and machine-learning applications.

Design and implement lakehouse data platforms using Microsoft Fabric and Databricks, build scalable ETL and ELT pipelines, develop enterprise semantic models, and deliver advanced Power BI dashboards. Translate client requirements into secure, performant analytics solutions while providing technical consulting, stakeholder guidance, training, documentation, and platform adoption support. Collaborate with project teams, contribute to data standards, and support business development through technical expertise.

Own the go-to-market data infrastructure, integrating HubSpot, Dreamdata, advertising platforms, PostHog, and website data into BigQuery. Diagnose tracking and attribution discrepancies, configure multi-touch revenue attribution, and build executive dashboards covering channel performance and revenue. Develop ABM workflows using Clay, enrichment, behavioral signals, and AI personalization. Partner with sales and marketing to improve funnel visibility, campaign decisions, and outbound effectiveness.

Build and improve data pipelines, integrations, and AI agents for Starlink Enterprise. Clean and enrich CRM and go-to-market data, develop MCP servers and connectors, instrument data and agent performance, and partner with sales and ops to automate workflows and reduce manual cleanup.

Improve go-to-market data quality and build integrations and AI agents for Starlink Enterprise. Implement validation, enrichment, deduplication, MCP interfaces, and automations across CRM and operational systems. Instrument data and agent performance, partner with sales and ops, and ship reusable tools and monitoring to accelerate lead-to-revenue workflows.

Design, build, and maintain scalable Hadoop-based data processing pipelines and ETL for structured and unstructured data. Implement streaming and batch solutions (Spark, Storm, Kafka), integrate multiple data sources and NoSQL stores, optimize performance, define retention, and collaborate with product and data science teams in an Agile environment.

Design, implement, and own reliable data solutions across design, manufacturing, and mission operations. Lead data architecture, migrations, ETL/replication, schema design, and optimizations. Partner with cross-functional teams, drive tradeoffs, prioritize work using data, mentor team members, and deliver production-ready data products.

Design, build, and maintain scalable cloud-native data platforms and pipelines (batch, streaming, near-real-time). Implement distributed processing (Spark), event-driven systems (Kafka), governance, security, observability, and support ML/analytics. Partner with cross-functional teams, optimize performance/cost, and mentor engineers while enabling production-grade data products.