Top Data Engineer Jobs

As a Data Engineer, you will develop and deploy data solutions, maintain data stores, and create ETL/ELT workflows to organize data for business analysis in a collaborative environment.

Design, build, and maintain enterprise ETL/ELT pipelines and data management workflows for market, reference, and financial data in the Markit EDM platform. Implement ingestion, validation, reconciliation, enrichment, monitoring, and distribution processes; support reporting and downstream integrations. Troubleshoot production data issues, collaborate with stakeholders in agile squads, and mentor junior engineers to promote best practices.

The role involves developing scalable data pipelines, automating workflows, optimizing data models, and ensuring data governance while collaborating with various teams.

Design, develop, and maintain data pipelines and ETL workflows in Databricks, collaborating with teams to support large data needs in financial services.

As a Data Engineer, you will design and build robust data pipelines, manage data infrastructure, and support data-driven strategies using advanced engineering techniques.

Looking for a Data Engineer focused on data visualization with experience in handling real-world data and effective communication through data. Extra credit for skills in probabilistic classifiers and full-text search engines.

The Data Engineer will design and maintain data pipelines, optimize cloud data warehouses, and collaborate with analytics teams while mentoring junior staff.

As a Data Engineer I, you will design, build, and operationalize data solutions, manage data governance, and collaborate with teams to optimize analytics processes. Responsibilities include developing pipelines, troubleshooting issues, and mentoring team members.

Design, build, and maintain scalable ETL/data pipelines and data models; ensure data quality and governance; collaborate with data scientists, analysts, and stakeholders; optimize performance and document data systems; evaluate and adopt data platform technologies.

The Data Engineer will design, build, and maintain scalable data pipelines, develop ETL workflows using Python and SQL, and optimize data processes to ensure reliability and performance.

The Data Engineer is responsible for designing and maintaining data architecture, implementing data pipelines, and collaborating with cross-functional teams to align data strategies with business needs.

Build and maintain AWS-based ETL pipelines (Glue/PySpark, MWAA/Airflow, Lambda) to ingest and process terabytes of financial data into an S3 data lake using Iceberg/Parquet. Implement metadata, monitoring, tests (90% coverage), materialized views (Trino/Athena), CloudFormation deployments, documentation, and support agile delivery and operations for federal clients.

Design, build, and maintain scalable end-to-end data pipelines and ETL processes using Databricks, Spark, Python, Scala, and related tools. Ensure data quality, governance, integration, and performance for large federal datasets while collaborating with cross-functional teams and supporting analytics and decision-making.

Design, build, and maintain data connections and transformations to normalize external portfolio data into a standard Clearwater format. Automate monitoring, improve auto-reconciliation, implement solutions from data feeds, support projects and migrations, mentor hires, and communicate technical details to non-technical stakeholders.

Design, build, and support scalable data platforms and pipelines (ETL, warehousing, APIs) using DBT, Snowflake, and SQL. Collaborate with architecture, security, and application teams, build CI/CD, provide escalated on-call support, mentor staff, and deliver data solutions for the bank.

Design, develop, and maintain Power BI BI solutions: build data models, create reports and dashboards, write DAX and Power Query transformations, query and integrate data with SQL, apply data warehousing/ETL principles, optimize performance, document solutions, troubleshoot issues, and collaborate with stakeholders.

Lead client discovery and design scalable Lakehouse/warehouse analytics platforms on Azure and Microsoft Fabric. Implement ingestion, transformation, orchestration (SQL, Spark, KQL), build analytics-ready models, enable compute engines, design AI-enabled agentic solutions, enforce governance/security, establish monitoring/runbooks, and produce consulting deliverables and enablement for handoff.

Design, build, and maintain enterprise-scale data platforms and scalable ETL/ELT pipelines. Develop and operationalize data transformations in Palantir Foundry, debug logs, troubleshoot issues, and convert varied data into usable formats. Guide cross-functional teams, support platform scaling, monitoring, and operations, and apply PySpark/Spark SQL for analytics and reporting to support mission-driven use cases.

Design, implement, and administer operational data flows: ingest, clean, enrich, transform, secure, and move mission-critical data. Perform exploratory data analysis, troubleshooting, and collaborate with customers to ensure compliant, reliable data pipelines.

Build and operate scalable data ingestion, transformation, and connector frameworks; design and maintain a knowledge-graph-based data fabric; normalize and vectorize enterprise data for LLM/AI workflows; implement governance, lineage, access controls, and secure APIs to serve ML/agent pipelines.

Build, maintain, and optimize ETL/ELT pipelines and cloud-based data environments (AWS/Azure). Manage relational databases and data warehouses, ensure data quality, automate workflows, and support aviation analytics and aircraft readiness initiatives at MCAS Cherry Point.

Design and build end-to-end geospatial data systems: ingest and process satellite imagery into columnar/chunked stores, expose results via GraphQL/REST and FastAPI, build Next.js/React dashboards, automate with IaC and CI/CD, orchestrate pipelines with Prefect, optimize petabyte-scale I/O, and lead hiring, mentoring, and engineering best practices.

Design, build, and maintain scalable data pipelines, warehouses/lakes, and platforms. Optimize data lake performance, implement governance/catalog solutions, support batch and streaming workflows, and lead cross-functional teams on mission-driven analytics projects.

Serve as a senior technical lead for data engineering consulting: work with customers, design complex data models, write and review efficient code, resolve complex bugs, mentor engineers, lead cross-functional development, and plan/execute automated and performance tests to deliver high-quality data solutions.

The Site Industrial Data Engineer enhances data management and performance reporting in manufacturing, overseeing data integrity and reporting systems. They support digital solutions and collaborate with site leadership for continuous improvement of site performance KPIs.