Top Data Engineer Jobs

Design and develop scalable data solutions and tooling to enable AI and advanced analytics across Azure hardware and software stacks. Identify opportunities for AI-driven impact, build data platforms and visualizations, and collaborate with engineers, data scientists, and program managers to improve efficiency, quality, and operational insights.

Design, develop, and maintain Customer Data Platform solutions and data pipelines to create unified customer profiles, manage identity resolution, enable audience segmentation and activation, ensure data quality and governance, and support integrations with marketing, CRM, and cloud data systems.

Design, build, and maintain ETL/ELT pipelines and API integrations to ingest, cleanse, transform, and analyze infrastructure and cloud data. Produce analytics, dashboards, and executive reports; automate workflows and support near-real-time processing. Follow SDLC/DevOps practices (Git, CI/CD), ensure data governance, quality, lineage, and support incident, release, and compliance activities.

Designs and implements cloud-first, event-driven data solutions and ETL pipelines for data warehouses (Snowflake/Oracle). Leads architecture, CI/CD, QA, and Agile delivery; mentors teams; builds scalable backend systems using AWS, APIs, and automation tools. Ensures project delivery on time and within budget while advising senior management on technical strategy.

The Senior Data Engineer will design and maintain data pipelines, manage a Snowflake environment, establish data quality, and lead data initiatives across teams, influencing company-wide technology decisions.

Designs and maintains ETL pipelines, data architectures, databases, data models, and master data management solutions. Analyzes, profiles, cleans, and validates complex datasets; establishes data quality standards; and develops automated data management processes. Partners with architects, data stewards, and business stakeholders to improve data integrity and support business requirements. Communicates insights through reports and presentations while using cloud, database, streaming, CI/CD, and data modeling technologies.

Design and optimize the enterprise data warehouse and ELT pipelines (batch and real‑time). Integrate Databricks, CRM, and external systems into a governed, observable data platform. Drive automation, cost optimization, and monitoring; mentor junior engineers; and partner with Analytics, Ticketing, and Marketing to deliver scalable data solutions that support reporting and ML initiatives.

Design, build, and maintain scalable data pipelines, client integrations, and data models to support reporting, analytics, and AI/ML. Ensure data quality, monitoring, and reliable ingestion of healthcare (FHIR/HL7, EHR) and claims data. Collaborate with product, engineering, and AI teams to enable downstream products and analytics.

The role involves developing and maintaining the Enterprise Data Warehouse, optimizing queries, and supporting data analysis and business intelligence activities.

Design and manage scalable data pipelines and infrastructure, implement ETL/ELT processes, and collaborate with teams to ensure data quality and accessibility.

The Snowflake Data Engineer will work on data cleansing and harmonization, moving from legacy systems to SAP S4, focusing on ETL/ELT development and view creation.

This role involves designing, building, and maintaining data pipelines on Snowflake. Responsibilities include ensuring data quality and collaborating with stakeholders to deliver solutions.

The AWS Data Engineer will work on a data cleansing and harmonization project, focusing on AWS ETL design and development using various AWS services.

The Snowflake Data Engineer will design, build, and maintain data pipelines on Snowflake, optimizing performance and ensuring data quality while collaborating with stakeholders.

The Snowflake Data Engineer and Product Owner will manage the Snowflake data analytics project, collaborating with stakeholders, leading Agile practices, and ensuring quality deliverables through effective team coordination and backlog management.

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.