Top Data Engineer Jobs

Design, develop, and optimize Teradata data solutions: elicit requirements, create logical/physical designs, write SQL (views/procs/macros), use Teradata utilities for data loads, tune performance for large datasets, coordinate stakeholders, and write Unix shell scripts.

Elicit and analyze requirements; design logical/physical Teradata solutions; develop ETL using Teradata utilities; write SQL (views, stored procedures, macros, functions); perform performance tuning for large datasets; write Unix shell scripts; coordinate with stakeholders and support BI/DW initiatives.

Design, build, and maintain scalable ETL/ELT pipelines ingesting travel-related data from multiple sources. Integrate and model data in cloud warehouses (Snowflake/BigQuery/Redshift), implement data quality and monitoring, support real-time streaming, and ensure data governance and regulatory compliance. Collaborate with analysts, data scientists, and product teams to deliver reliable datasets for personalization, forecasting, and optimization.

Build and own customer data ingestion and large-scale pipelines powering AI workers. Design syncing, retrieval, and data lake systems, improve reliability/observability, partner with product/backend teams, and contribute across infrastructure to give AI agents access to enriched customer data.

The Data Engineer will maintain and optimize data pipelines, manage infrastructure, support ML workflows, and collaborate with teams on data products.

Design, develop, test, maintain, and support secure, scalable data solutions using Snowflake and Databricks. Lead technical design, author specifications, review code, mentor engineers, ensure SDLC, audit/security compliance, and drive technical strategy while collaborating with architects and business partners. Participate in Agile ceremonies, be on-call as needed, and travel occasionally.

As an Analytics Engineer, you'll manage the data stack, design database architecture, build data pipelines, and oversee reporting and data quality.

Design, build, and maintain batch and real-time ETL pipelines and data lake architecture using AWS services, Python, PySpark, and SQL. Ensure data quality, governance, and compliance; support reporting and ML models; mentor junior engineers and document data products.

Design, build, and maintain scalable data ingestion and ETL pipelines across a growing data lake. Define and enforce data quality standards, SLOs, and validation frameworks. Optimize pipelines for performance and cost, expand monitoring and alerting, and enable stakeholders to self-serve on data infrastructure.

Design, build, and maintain enterprise-scale data platforms and ETL/ELT pipelines. Develop software and scripts to ingest, parse, and process structured and unstructured data. Collaborate with cross-functional teams to scale, monitor, and operate data solutions using Databricks, Spark, and CI/CD practices to support mission-driven analytics.

Lead design, build, and deployment of secure, scalable data pipelines and analytics platforms (including Palantir) in constrained or classified environments. Mentor multidisciplinary teams, integrate AI/ML and LLM capabilities into production workflows, implement DevSecOps practices, and communicate architectures to technical and nontechnical stakeholders.

Design, build, and maintain scalable cloud data platforms and ELT pipelines using AWS, Snowflake, and dbt. Implement data models, orchestration, IaC, data quality, monitoring, and CI/CD. Support production operations, compliance, and mentor junior teammates.

Build and maintain Azure Data Factory ingestion pipelines into Snowflake, implement medallion transformations (Coalesce), write Python orchestration and connectors, apply governance with Atlan, collaborate with AI coding agents, participate in CI/CD with Azure DevOps, handle on-call production support, and document runbooks.

Design, build, and maintain data transfers and databases. Produce efficient, reusable data engineering code, perform data modeling, UAT, data cleaning, reporting, and technical documentation. Collaborate with stakeholders, participate in code reviews, and improve automation and processes to meet business requirements.

Build and maintain data infrastructure to measure agent behavior and user interactions, design evaluation frameworks for AI agents, analyze user behavior to inform product strategy, and partner with engineering and leadership to solve high-impact business problems using data.

Design, build, and maintain ETL/ELT pipelines and enterprise data products. Monitor data pipelines, support data modeling and semantic layers, enforce governance, and collaborate with analytics and AI teams to deliver context windows and embeddings for AI/ML workflows.

Lead and mentor a data engineering team, define technical vision and strategy, oversee data pipeline design, and collaborate with cross-functional teams.

Build and maintain scalable data pipelines, trusted datasets, and reporting systems to support infrastructure deployment, hardware operations, capacity planning, and supply chain analytics. Partner with stakeholders, improve data quality, lineage, and observability, and enable executive reporting, forecasting, and operational reviews. Integrate new telemetry sources and document systems for long-term maintainability.

Lead design and delivery of complex data solutions including data integration, cleaning, transformation, lineage, and migration. Build and document data pipelines, manage ADS and regulatory data lineage, lead testing and cutover for database migrations, create automation and reconciliation scripts, troubleshoot with architects and security teams, and mentor other data engineers.

The Data Engineer will execute data architecture projects, manage data ingestion processes, ensure data quality, and transition data to new systems while collaborating with investment teams.

As a Senior Data Engineer, you will design and maintain data infrastructure, implement ETL processes, and optimize data workflows using Python and PySpark.

Design, build, and operate scalable cloud data pipelines and ELT/ETL processes (batch and streaming) into Snowflake. Automate monitoring, triage incidents, optimize performance and costs, enforce access controls, and provide on-call support. Collaborate with Product, Platform, and Architecture teams, document runbooks, and mentor junior engineers while researching emerging tech and improving observability and CI/CD for data deployments.

Lead architecture and delivery of cloud data platforms, design scalable data warehouse and lake solutions, implement ETL/ELT frameworks, optimize platform performance and resilience, champion automation, governance, and mentor engineering teams to deliver enterprise analytics and data products.

Design, build, and maintain production-grade ETL/ELT pipelines and data models using dbt, Spark, and cloud platforms. Manage ingestion, data quality, observability, semantic layers, and BI outputs. Collaborate with analysts and data scientists to deliver governed, high-quality data products and implement DataOps and CI/CD practices.

Design, build, and maintain AWS-integrated data pipelines and infrastructure. Translate business needs into schemas and data dictionaries, optimize for scalability, ensure data governance, troubleshoot stakeholders' issues, and provide Tier 3 support for deployed applications and dataflows.