Top Data Engineer Jobs

Design, build, and maintain scalable GCP data architectures and ETL/ELT pipelines (batch and streaming) using BigQuery, Dataflow, Apache Beam, Cloud Storage, and Pub/Sub. Implement performance tuning, data quality and governance, secure access controls under federal requirements, and automated CI/CD for deployments. Collaborate with data scientists and infrastructure teams to modernize legacy systems and optimize data models for mission use.

Design, build, and deploy production-grade data and AI solutions on Databricks. Create scalable data pipelines, APIs, and Databricks Apps, integrate LLMs/AI APIs, deploy to cloud, implement CI/CD and governance (Unity Catalog), debug full-stack issues, and provide hands-on technical guidance to clients.

As a Senior Lead Data Engineer, you will design, develop, and support data solutions, optimize systems, and collaborate within Agile teams to leverage technology for business transformations.

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.

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.

As a Data Engineer, you will design, maintain data pipelines, collaborate with stakeholders, and optimize data solutions across various departments.

Design and maintain scalable ETL/ELT pipelines, develop data solutions using Databricks and Snowflake, and collaborate with cross-functional teams to ensure data quality and performance optimization.

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.

Design, build, deploy, and maintain large-scale data pipelines and ETL using Python and Spark on Azure (Synapse/Databricks). Collaborate with cross-functional teams to translate requirements into data designs, implement CI/CD, monitor and troubleshoot production pipelines, ensure data security and governance, perform code reviews, and document solutions while driving automation and process improvements.

Design, build, and optimize large-scale Databricks/Spark/Delta Lake data pipelines and Redshift data platforms. Implement ETL/ELT patterns, ingest telemetry and behavioral data (Matomo, FullStory, Pendo), improve performance and observability, provide L2/L3 production support and RCA, and mentor engineers while shaping data architecture and best practices.

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.

Design, build, and optimize scalable Azure-based data pipelines and data warehousing solutions. Implement ETL/ELT, data cleansing, validation, batch and real-time processing, monitor and troubleshoot pipeline performance, ensure data governance and security, document data flows, and collaborate with analysts and stakeholders to deliver reliable analytics-ready data.

Design, develop, and manage data infrastructure for internal and client projects, focusing on large-scale data pipelines, APIs, and cloud deployments.

The role involves designing and managing data pipelines, developing canonical datasets, and ensuring data integration and compliance. Collaborate across teams to meet data needs and maintain data architecture.

As a Data Engineer, you will build and scale data pipelines, optimize advertising effectiveness, and handle large datasets using AWS, Airflow, and Snowflake.

As a Data Engineer, you will create efficient data models, pipelines, and analytics frameworks, collaborating with various departments to transform raw data into actionable insights.

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.

Embedded data engineer on the systematic China equity team responsible for end-to-end data lifecycle: sourcing, ingestion, modeling, validation, transformation, and delivery for research and production. Build scalable data pipelines, schemas, quality checks, and monitoring; profile datasets; collaborate with researchers, developers, and vendors; and improve tooling, standards, and vendor relationships to support alpha research and trading.

The Data Engineer will build and maintain scalable data pipelines, ensure data quality, and support a metadata-driven framework while collaborating with stakeholders.

Design, build, and maintain scalable AWS-based data pipelines and storage (S3, Redshift, RDS, DynamoDB). Implement IaC (CloudFormation/CDK), ETL optimization, monitoring, security (IAM, OWASP/CIS best practices), and CI/CD/code pipeline controls. Collaborate with product teams, document solutions, and provide training to support analytics and reliable data infrastructure.

Job posting contains only company and EOE information. No Data Engineer responsibilities, qualifications, or requirements are provided in the description.

Build and maintain reliable ETL and real-time data pipelines, enable data-driven product decisions, ensure relevant event collection, align data roadmap to business goals, and collaborate with platform, security, and analytics teams to deliver actionable insights.

Design, build, and maintain data warehouse infrastructure, semantic/dimensional models, metrics layer, and BI dashboards (Sigma, Power BI, Databricks). Build secure ELT pipelines, enforce governance and row-level security (Entra ID), enable self-service analytics and AI/ML integrations, triage BI issues, train stakeholders, and collaborate in Agile to deliver reliable, performant reporting and curated data products.

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.