Top Data Engineer Jobs

As Lead Data Engineer, you will guide a team, manage the data platform, and oversee the development of ML infrastructure and feature stores.

Lead design, build, and optimization of a Snowflake-centric cloud data platform and dbt transformation layers. Develop reliable pipelines from sources like Salesforce, ensure data quality, performance tuning, and compliance with PHI/PII/HIPAA. Collaborate with analytics, product, and engineering teams to deliver scalable, documented, analytics-ready datasets and platform standards.

The Data Engineer will enhance data pipelines, support data analysts, and optimize data architecture for cross-functional teams. This role involves data extraction, transformation, and analytics to meet reporting needs.

The Data Engineer will design, build, and optimize data pipelines, ETL workflows, and large-scale data solutions, with strong SQL expertise required.

The Data Engineer will manage data pipelines for financial institutions, translating raw transactions into insights while collaborating with various teams to enhance data models and tools.

The DBA/Data Engineer will manage PostgreSQL and MySQL databases, build scalable data pipelines, and integrate AI for automation, ensuring efficient data infrastructure.

Build and operate scalable data pipelines and analytics-ready datasets using Python, dbt, and BigQuery. Develop Looker dashboards, ensure data quality and observability, optimize performance and cost, integrate data workflows with backend services and CI/CD, and support analytics, experimentation, and production use cases.

Design, build, and maintain scalable ETL/ELT pipelines and data warehouse architectures. Automate ingestion, transform and validate data, enforce data governance, optimize queries, troubleshoot production issues, and collaborate with stakeholders to deliver timely BI and analytics solutions.

Design, implement, optimize, and maintain scalable, high-availability database solutions for a SaaS healthcare product. Integrate client data, resolve complex data issues, refactor systems for Azure, optimize MSSQL performance, participate in code reviews, and deliver features within Agile/Scrum to ensure reliable, high-performance data services.

Design, build, and maintain scalable finance-focused data pipelines and models; ensure data quality, manage migrations to modern stacks, monitor loads, troubleshoot issues, and collaborate with global teams and vendors to support reporting and analytics.

Lead design and delivery of enterprise data centralization and automation for reporting and BI. Build and maintain ETL/SSIS pipelines, data orchestration, file delivery and quality controls, follow SDLC/agile, mentor junior engineers, and partner with business stakeholders to capture requirements and ensure timely data delivery.

Lead modernization of core data systems and build scalable, secure data pipelines for cloud and on‑prem environments. Design data contracts, observability, lineage, and real‑time processing for vulnerability management. Partner with product and leadership to align data strategy and deliver customer-facing data solutions.

Build and maintain scalable batch and real-time data pipelines using Kafka, Google Dataflow, Snowflake and S3. Integrate APIs via Apigee, ensure data quality, publish data for reporting, and collaborate with stakeholders to document workflows and support analytics.

Design, build, and maintain scalable ETL/ELT data pipelines on Databricks and AWS S3; manage large geospatial and temporal datasets; productionize ML models; implement data validation, testing, and monitoring; optimize storage and processing; document workflows; collaborate cross-functionally.

Lead design, implementation, automation, and maintenance of large-scale ETL pipelines and scalable data solutions. Translate business questions into data requirements, validate and test datasets, mentor junior engineers, collaborate with analytics and stakeholders, and ensure robust, production-ready data solutions across global clients.

As a Senior Data Engineer, you will design and maintain data pipelines, define governance standards, and collaborate across teams to enhance Headway’s data architecture.

As a Data Engineer III, you will develop data solutions, lead projects, mentor peers, and ensure efficient data pipelines within cloud architectures.

Design, build, test, and support cloud-based data solutions; collaborate with Agile teams and product managers; develop full-stack and distributed data systems using big-data, streaming, and warehousing technologies; perform unit tests and code reviews; mentor peers and stay current with emerging technologies.

Lead design, develop, test, implement, and support cloud-based data solutions. Collaborate across Agile teams on distributed, streaming, and full-stack systems; perform unit testing, code reviews, mentoring, and drive adoption of modern data warehousing and big-data technologies.

Design, develop, test, implement, and support cloud-based data solutions; collaborate across Agile teams; build distributed, streaming, and data warehousing systems; write and review performant code; mentor engineers and stay current with emerging technologies.

Lead cross-functional Agile teams to design, build, test, and support cloud-based data solutions. Develop full-stack data engineering systems using Java/Scala/Python, RDBMS and NoSQL, Redshift/Snowflake, and distributed tools (Hadoop, Spark, Kafka). Mentor engineers, perform code reviews and unit testing, and collaborate with product managers to deliver scalable, performant data products.

Design, build, test, and support cloud-based data solutions and streaming applications. Collaborate with Agile teams, mentor engineers, implement distributed computing and data warehousing (Redshift/Snowflake), and ensure performant, well-tested code for production systems.

Design, develop, test, implement, and support cloud-based data solutions. Collaborate with Agile teams on distributed, real-time, and full-stack systems; write and review performant code; mentor engineers and adopt emerging data technologies.

The Data Engineer will develop Python data applications, collaborate with teams to optimize data solutions, and maintain production operations support.

Build and maintain cloud data pipelines and ML feature/model workflows in Snowflake and AWS. Implement data ingestion, transformations, quality checks, monitoring, and documentation. Collaborate with data scientists, analysts, and product teams to support analytics, audience insights, rapid prototyping, and reliable production deployments.