Top Data Engineer Jobs

The Lead Data Engineer will design, develop, and support complex technical solutions using Java, Python, AWS, and big data technologies. Responsibilities include collaborating with Agile teams, mentoring, and delivering cloud-based solutions.

The job involves leading a team to develop data-driven solutions, utilizing various programming languages and cloud technologies, while collaborating with Agile teams and mentoring junior members.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

Build and maintain scalable, governed data pipelines from ingestion through transformation and integration. Implement schema validation, data quality checks, job monitoring, and production-grade APIs. Work with Python, SQL, Airflow, dbt, AWS (EMR, Athena, S3), Iceberg/Parquet, and contribute to CI/CD, observability, and on-call support while collaborating with analytics and product teams.

Consult on and design complex enterprise data ingestion, curation, provisioning, models, and architecture to enable analytics, reporting, and data science. Develop solutions, tooling, and process improvements, document systems, support implementations and transitions, and collaborate with teams and vendors. Provide observability-enabled application design and post-implementation support.

Build and maintain internal data application tools and frameworks for a centralized cloud data lake. Collaborate with cross-functional teams and Data Engineers to add features and maintain Go-based data pipelines. Improve platform architecture for scalability, observability, and reliability while sharing knowledge and mentoring teammates.

Lead architecture and reliability of the data platform, build and harden ingestion (batch and CDC), own BigQuery+dbt transformations, establish testing/observability/governance, mentor engineers, develop Python tooling and APIs, run POCs for lakehouse/open table formats, and define AI-assisted development practices.

Lead Data Engineer designs, builds, and supports cloud-based data solutions for risk management. Collaborates across Agile teams to implement distributed, real-time, and ML-enabled data platforms using cloud, big data, and database technologies, mentors engineers, and ensures code quality and performance.

Lead design, develop, test, deploy, and support cloud-based data solutions and pipelines. Collaborate with Agile teams, product managers, and engineers on distributed, streaming, and full‑stack systems; mentor engineers; optimize code and perform unit testing and reviews to drive marketing technology data capabilities.

Design, build, and operate scalable cloud-native ETL/ELT pipelines and data platforms (Databricks/Snowflake). Ensure data quality, governance, performance tuning, CI/CD, and support analytics and AI/ML workloads while mentoring junior engineers.

Design, build, and maintain scalable cloud-based data pipelines and models (AWS, Snowflake). Implement ETL/ELT, data quality, automated tests, monitoring, and troubleshooting. Collaborate with product owners, data scientists, and engineers to translate business requirements into reliable data solutions and document data flows and technical specifications.

Design, build, and own the full data platform (ingestion, modeling, CI/CD, orchestration, observability, and BI). Implement streaming and batch pipelines for IoT data, enable governed self-service, enforce data governance/privacy/security, evolve AI access patterns, and provide technical leadership and mentorship to data engineers and data scientists to deliver production-ready analytics and ML capabilities.

Lead design, build, and operate scalable, secure GCP data platforms and reusable data frameworks. Implement ingestion, transformation (batch and streaming), orchestration, data modeling, governance, observability, and security controls. Enable self-service consumption, provide enablement, and contribute to platform roadmap and CI/CD-enabled deployment practices.

Lead design, build, and operate reusable, secure, cloud-native data platforms on Google Cloud. Create ingestion, transformation, orchestration frameworks, enforce governance, optimize cost/performance, enable self-service analytics and AI, and provide enablement and runbooks for product and domain teams.

Lead design, build, and operate scalable, secure GCP data platforms and reusable frameworks (ingestion, transformation, quality, orchestration). Enable self-service consumption and governance-by-design, optimize performance and cost, implement data modeling and observability, enforce security controls, partner with domain teams to onboard use cases, and contribute to platform roadmap and enablement.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

Belvedere Trading seeks a Senior Data Engineer to enhance trading performance through data availability and reliability, designing ETL pipelines and data architectures in a fast-paced environment.

Build scalable cloud data infrastructure and pipelines to transform transactional and financial data into reporting-ready datasets. Ensure accuracy, traceability, governance, and observability; partner with cross-functional teams, mentor engineers, and leverage AI-assisted tools to improve engineering productivity and enable AI-powered applications.

As a Lead Data Engineer, you will collaborate with Agile teams, develop technical solutions, and enhance cloud-based data warehousing to empower financial services for customers.

Lead Data Engineer designs, develops, tests, and supports cloud-based, distributed data solutions. Collaborates across Agile teams, mentors engineers, works with big data, streaming, NoSQL and data warehouse technologies, and ensures performant, well-tested code for customer-facing products.

Design, build, test, and support cloud-based data platforms and real-time pipelines. Collaborate across Agile teams to deliver distributed microservices and data solutions using big data technologies, cloud data warehouses, streaming tools, and modern programming languages. Mentor peers, participate in code reviews, and ensure performant, well-tested systems that power customer experiences.

Design, build, and productionize batch and streaming ETL/ELT pipelines and ML workflows on Azure Databricks. Implement Unity Catalog data products, MLflow model lifecycle controls, CI/CD for data/ML artifacts, and operational monitoring. Partner with cross-functional teams to modernize legacy workflows into scalable, production-ready solutions.

The Lead Data Engineer will develop full-stack solutions, collaborate within Agile teams, and work with big data technologies and cloud computing to improve predictive insights for decision making.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.