Top Hybrid Data Engineering Jobs

Lead and deliver large-scale data processing and integration initiatives. Build and maintain optimized, highly available ETL pipelines, data models, and data warehouses. Resolve technical issues, ensure compliance, and collaborate with teams to meet strategic data engineering goals.

Lead and manage projects building modern data ecosystems and managed data services. Oversee strategic planning, mentor teams, ensure project delivery and quality, design data strategies, and convert analytics into business opportunities.

Lead engineering for the Data & Analytics Solutions portfolio, driving technical execution, product evolution, process governance, and business growth. Functionally lead Program Engineering Managers, oversee lifecycle activities, technical bid/design reviews, and collaborate with product, portfolio, and chief engineering leadership. Hybrid onsite/remote role supporting IC and DOW customers.

The Geospatial Data Engineer designs and implements data architectures and systems for intelligence analysis, supporting GEOINT analysts with automation and model creation.

Design, build, and maintain cloud-based data pipelines and products (batch and streaming). Develop microservices, orchestration, and feature stores in AWS. Partner with stakeholders, automate ETL, enable self-service analytics, and mentor junior engineers.

The Data Engineer will build datasets and pipelines for marketing and sales reporting, collaborating with various teams to support data needs and ensure scalable solutions.

Lead Data Engineer responsible for designing, building, testing, and supporting cloud-based data platforms and streaming solutions. Collaborate across Agile teams, mentor engineers, apply big data and distributed computing tools, and deliver performant, production-grade data services that support product and ML use cases.

Summer internship on the Data & Analytics Platform team building automation, model prototypes, and platform features. Work with engineers to write, test, and deploy code, partner with internal customers to prototype ML and generative workflows, and participate in design discussions and code reviews in an agile environment.

The analyst will develop data pipelines, design automated reporting solutions, conduct data analysis, and maintain data quality to support business decision-making.

Design, build, and optimize scalable data pipelines and products using Databricks, Spark, and Azure to support real-time and historical vehicle health analytics, ensuring data quality, governance, and CI/CD for ML workflows.

Develop and implement advanced algorithms, apply statistical techniques, collaborate with clients, assess technologies, and mentor team members.