Top Data Engineer Jobs

The Data Engineer will develop tools and code for data engineering activities, support data transport and ingestion, and work alongside Data Scientists. The role requires hands-on coding and ETL responsibilities, ensuring data is correctly formatted for analysis.

Design, build, test, and maintain data architectures, database systems, and pipelines using GCP and BigQuery. Optimize data retrieval, automate processes, redesign infrastructure for scalability, collaborate with stakeholders, and deliver analytics tools that provide actionable business insights.

The Data Engineer is responsible for building and maintaining data pipelines, ensuring data quality and reliability, and collaborating with various teams on data needs. This involves working with AWS tools and programming in Python and SQL to support analytics and infrastructure.

The Cloud Data Engineer will design, implement, and manage data solutions on Microsoft Azure, create and maintain data pipelines, and ensure data security for decision-making processes.

Design, build, and operate data pipelines for structured and unstructured sources, performing ingestion, cleaning, transformation, quality checks, lineage capture, and monitoring. Ensure compliance with DoD governance and security, troubleshoot pipeline failures, maintain runbooks, and collaborate with stakeholders to deliver scalable, governed data products on Army enterprise platforms using Vantage/Foundry tools.

As a Data Engineer, you will develop solutions for customer-centric technology, ensuring platform integration and managing data architecture. Responsibilities include building data processes, maintaining data integrity, and collaborating cross-functionally to meet business challenges.

As a Data Engineer at OpenAI, you'll design and implement data pipelines, develop key datasets, and collaborate with teams to integrate data for People Innovation Labs' products.

The Mid-Level Data Engineer will design and optimize data pipelines and services, supporting analytics and machine learning readiness in an Azure environment.

Design, build and support EDW/data mart and Hadoop-based data ingestion and integration solutions. Support development, SIT and UAT investigations, coordinate with users, and deliver reliable data pipelines.

Design, build, and maintain scalable data pipelines and platforms for banking data. Collaborate with teams to ensure secure, reliable data products.

Lead design and delivery of Microsoft Fabric and Azure-based data lakehouse and warehouse solutions. Own data ingestion, ETL, modeling, governance, security, CI/CD, and Power BI integration. Architect GenAI/RAG solutions, mentor a small engineering team, define standards and playbooks, and partner with business and tech stakeholders to execute multiple concurrent implementations.

Design and build scalable cloud-native data pipelines and models to produce trusted, analysis-ready data. Ensure data quality, governance, and privacy controls, apply software engineering best practices, enable BI and AI/ML consumption, and collaborate with clients and cross-functional teams.

Build and maintain ELT pipelines, data storage and quality frameworks, and observability for enterprise systems. Support AI/ML feature pipelines, ensure compliance with FDA/ITAR/DCAA, evaluate data platforms, and implement modern development practices (source control, CI/CD). Collaborate across functions to translate business requirements into production-grade data solutions.

Design, build, and maintain data infrastructure and ETL pipelines; automate data ingestion; transform and model data; integrate data for applications, dashboards, and ML pipelines; ensure data quality, governance, and performance; collaborate cross-functionally and communicate results to leadership.

Design and build scalable data pipelines for Investment Data Standard and knowledge graph, implementing data quality controls and collaborating with cross-functional teams.

Lead design and delivery of scalable data pipelines and APIs for Digital Risk Protection. Mentor engineers, align and ingest multiple data streams (including graph data), productize analytics and LLM outputs, and drive DRP technical projects with stakeholders to enable data-driven intelligence.

Design and build scalable batch and real-time data pipelines on Databricks and Azure. Implement ingestion, ETL, transformation, analytics workflows, APIs and data products to support AI/ML and BI. Ensure data quality, reliability, performance, and CI/CD-driven DevOps practices while collaborating with cross-functional teams.

Maintain and monitor Snowflake data warehouse and ingestion pipelines. Troubleshoot file/load/data quality issues, operate SFTP/drop-zone/file automation, support scheduled loads, build dbt models and tables, document runbooks, and serve as backup for core warehouse operations while collaborating with engineering and IT.

Design and build data warehouse, lakehouse, and analytics solutions on Microsoft Fabric and Azure SQL. Implement Medallion Architecture, develop reliable ELT/ETL pipelines, model data for analytics, ensure data quality and monitoring, consult with clients on requirements and governance, and provide technical leadership and mentoring.

Build and operate low-latency, real-time data and ETL pipelines for sports analytics products. Support production systems during live events, develop predictive analytics and APIs, and integrate complex real-time datasets into consumer and enterprise offerings.

Design, develop, operationalize, and maintain enterprise-scale data applications and relational databases. Build scalable ETL/ELT pipelines, process structured and unstructured data, develop scripts for data conversion, and collaborate with AI/ML engineers to train, deploy, and maintain models supporting mission-critical solutions.

Design, develop, and optimize cloud-based ETL/ELT pipelines and data architectures using Informatica and Databricks in AWS GovCloud. Author and optimize SQL and Python scripts, build secure RESTful APIs, perform data validation, migrations from on-premises to cloud, and troubleshoot performance issues across large datasets. Support DON Jupiter enterprise environment and related ETL frameworks.

Build, deploy, and maintain secure, scalable data pipelines and analytics on AWS/Databricks. Implement CI/CD, governance (Unity Catalog, lineage), platform observability, and operational support for enterprise datasets and Databricks SQL reporting.

Design, build, and optimize scalable ETL/ELT data pipelines using Databricks and cloud platforms. Implement data quality checks, monitor and troubleshoot jobs, audit pipelines for compliance, and collaborate with analysts and business stakeholders to define data models and support reporting.

Build and maintain end-to-end event-driven analytics pipelines for telemetry from robots and cloud systems. Own metrics catalog, schema evolution, data quality validation, and performant analytical models and queries to enable reporting and self-service analytics.