Top Data Engineer Jobs

Design, build, and maintain end-to-end data ingestion, processing, storage, and access solutions. Implement ETL/ELT pipelines, data modeling, data lakes/warehouses, APIs, monitoring, and documentation. Collaborate with data scientists, analysts, and stakeholders to deliver scalable, secure, production-ready data services.

Design, build, and maintain scalable data pipelines and platforms. Implement ETL/ELT workflows, parse and process structured/unstructured data, and support data platform scaling, monitoring, and operations while working on cross-functional agile teams.

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.

Build and maintain scalable, event-driven data integrations and pipelines on cloud platforms. Collaborate with engineers, analysts, and stakeholders to design, test, deploy, and monitor CI/CD-backed data solutions, ensuring reliability, performance, and ongoing maintenance for K-12 online learning systems.

Lead design and delivery of scalable data lakes, warehouses, and pipelines. Define governance, quality, monitoring, and CI/CD. Build real-time streaming systems and production AI/ML data flows (embeddings, vector stores, RAG, feature stores). Mentor engineers, set platform strategy, and drive data platform modernization.

Design and implement Databricks-based Lakehouse pipelines (PySpark, Delta Lake, DLT), ensure data quality and governance (Unity Catalog), enable ML-ready datasets with MLflow, optimize performance, build CI/CD, and collaborate directly with clients and stakeholders.

Design, build, and maintain large-scale ETL/ELT data pipelines and cloud data architectures (AWS/Azure). Optimize Spark/Databricks/Synapse jobs, orchestrate workflows with Airflow, manage SQL/NoSQL databases, implement governance/security, create dashboards, integrate APIs, support ML model pipelines, and deploy using Docker/DevOps practices while monitoring pipeline health.

Design, build, and maintain data pipelines and platforms using Palantir Foundry; collaborate with analysts, engineers, and stakeholders; deploy scalable ingestion, transformation, and infrastructure automation; support agile teams and code reviews; work on mission-driven analytics requiring security eligibility.

Design, develop, and maintain scalable data pipelines, warehouses, and stores. Build ETL/ELT workflows, process structured and unstructured data with SQL and Python, validate databases, and support data platform scaling, monitoring, and operations for mission-driven clients.

Design, implement, and maintain scalable ETL pipelines to ingest and transform street and roadway data for transportation network analysis. Automate and optimize data workflows, collaborate with data providers and engineers, analyze routing errors, and document data models, workflows, and best practices.

As a Data Engineer at Figma, you'll build and maintain scalable data pipelines, optimize data models, and collaborate with teams to ensure timely data delivery.

Design, build, maintain, and optimize data infrastructure (databases, data lakes, warehouses) and ETL/ELT pipelines. Ensure data quality, security, performance, and compliance. Collaborate with analysts and stakeholders, document policies, and support monitoring, backup, and recovery strategies.

Support development and maintenance of data pipelines, cloud-based data platforms, and analytics solutions. Build and test ETL/ELT processes, develop scripts and queries (Python, SQL, PySpark), support AWS/Azure environments, implement CI/CD and Git workflows, monitor and troubleshoot pipelines, perform data validation/QA, document processes, and collaborate with stakeholders while growing technical skills.

Design, build, and maintain scalable data architectures, ETL/ELT pipelines, and data quality/monitoring to enable ML workflows, analytics, and Power BI reporting while partnering with Product, Engineering, and AI teams.

Lead product engineering and architecture for enterprise-scale data, analytics, and AI solutions. Define technical strategy, design Lakehouse and data product architectures, implement governed AI (LLMs, RAG, vector DBs), enforce data governance and modeling standards, drive platform adoption, mentor engineers, and partner with stakeholders to deliver secure, scalable data and Generative AI capabilities on Azure, Microsoft Fabric, and Databricks.

Design, build, and maintain scalable ETL/ELT pipelines and data platforms to ingest, parse, and process structured and unstructured data. Collaborate with cross-functional teams to deploy, monitor, and operate data solutions, convert diverse data formats, and support analytics, reporting, and real-time streaming use cases. Follow version control practices and meet security and clearance requirements.

Design, build, and maintain scalable data pipelines and platforms for analytics. Develop scripts to parse and convert structured and unstructured data, implement ETL/ELT workflows, collaborate with cross-functional teams, use version control, and support platform scaling, monitoring, and operations on mission-driven projects.

Lead design, development, and operationalization of enterprise-scale data applications and pipelines. Build scalable ETL/ELT workflows for structured and unstructured data, drive data architecture and migration efforts, optimize Informatica ETL, work with relational and cloud data stores, support CI/CD and DevOps, and ensure monitored, reliable data platforms to enable analytics and mission outcomes.

Design, build, and deploy scalable data pipelines and schemas to ingest and normalize disparate, multi-classification data for high-fidelity simulations. Integrate APIs, manage databases and data warehouses, and maintain ETL/ELT processes to support modeling, simulation, and data science teams in mission-critical space operations.

Design, build, and optimize GCP-based data architectures and ETL/ELT pipelines using BigQuery, Dataflow, dbt, and orchestration tools. Ensure data quality, security, and integration from diverse sources; collaborate with stakeholders to deliver actionable insights.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Lead operational design and engineering for the data platform: own Snowflake and dbt administration, automate platform operations and CI/CD, monitor platform health and observability, enforce governance and access, optimize performance and costs, and develop AI/agent integrations to enable governed data access for business users.

Support design, development, and maintenance of data pipelines and infrastructure. Assist with ETL/ELT workflows, data ingestion, cleaning, validation, modeling, and warehousing. Document processes and collaborate with data teams to support analytics use cases.

Set up test, preproduction and developer environments on physical servers; act as SQL Server DBA including security auditing; develop Big Data solutions and programs; perform user acceptance and integration testing. Requires experience with DTS/SSIS/SSRS, T-SQL, C#, PowerShell, Hive/Hadoop, and Microsoft SQL Server 2000 certification.

Build and own the core data models and pipelines connecting product usage, accounts, revenue, billing, and customer health. Create trusted metrics, dashboards, and self-serve reporting. Partner with Product, Engineering, and GTM teams to improve data quality, design AI-enabled workflows and agents, and support leadership with accurate visibility into adoption, pipeline, retention, expansion, and revenue.