
Data Engineer - Capital One (New York, Onsite)
Capital OneLead cross-cloud migration work for Capital One’s Travel Data team. Build Python, Spark, and Databricks pipelines that support personalized travel experiences.

Lead cross-cloud migration work for Capital One’s Travel Data team. Build Python, Spark, and Databricks pipelines that support personalized travel experiences.

Lead the architecture of Brown & Brown Insurance’s enterprise data and AI platform. Shape cloud, governance, MLOps, and Generative AI capabilities across retail technology.
Build self-service CI/CD, infrastructure-as-code, and governance tooling for ShippyPro’s global shipping platform. Own deployment workflows, reusable pipeline modules, and data-access processes.
Senior Data Engineer responsible for dependable pipelines, warehouses, and analytics infrastructure supporting GLS/NXT’s logistics technology products. The role focuses on data quality, modeling, testing, and scalable cloud platforms.

Develop machine learning, NLP, LLM, and RAG solutions for QAVION GROUP’s consulting and technology business. Build scalable data pipelines and production-ready generative AI services.

Build edge-to-cloud data infrastructure for autonomous scientific labs at the Ellison Institute of Technology Oxford. Support robotics telemetry, knowledge graphs, and machine-learning-ready datasets.

Build reproducible biological data pipelines as a Forward-Deployed Data Engineer at the Ellison Institute of Technology Oxford. Support AI, robotics, and life-science research by turning scientific data into practical resources.

Build secure Snowflake architecture, pipelines, and data models for Blood Cancer United. Support enterprise analytics and business intelligence that advance blood cancer patient outcomes.

Build data pipelines and integrations for HOLYWATER’s AI-powered streaming, books, and storytelling products. Improve data reliability, transformation workflows, and infrastructure efficiency.

Build secure AWS data platforms for MAPFRE’s insurance analytics and AI initiatives in an onsite Webster, Massachusetts role. Focus on infrastructure automation, CI/CD, DevSecOps, and cloud operations.

Build production data pipelines and agentic AI workflows for Citylitics’ infrastructure intelligence platform. Own data products from orchestration through customer-facing applications.

Senior Data Engineer developing AWS pipelines and integrations for Grupo S2. The role structures data for Analytics Engineering using Terraform, GitHub, and medallion architecture.

Lead enterprise data architecture, integrations, governance, and analytics for outsourced customer service operations. Manage data teams while advancing secure, scalable cloud platforms and supporting analytics and AI/ML initiatives.

Senior remote data engineer role supporting Monte Bravo by building data pipelines and a lakehouse architecture. Work with Python, PySpark, AWS, and dbt to develop an innovative financial platform.

Lead improvements to retrieval, embeddings, ranking, and relevance across Modash’s creator-discovery platform. Build search infrastructure at scale for brands managing influencer partnerships.

Lead the design, operation, governance, and modernization of EDC’s enterprise data and analytics platforms. Support business intelligence, advanced analytics, data engineering, and AI/ML workloads in a hybrid Canadian role.

Senior Data Engineer leading scalable Databricks platforms for RevoData, a European data and AI consultancy. The role covers architecture, pipelines, cloud solutions, governance, and client-facing technical leadership.

Develop data pipelines, integrations, and analytics solutions for Level Group’s BPO projects. Work with Python, SQL, Spark, Databricks, and Snowflake in a hybrid model.

Lead the design of SAP HANA Cloud and graph data foundations for DataXstream’s enterprise AI platform. Improve pipelines, security, performance, and integrations supporting machine learning and search.

Organize and standardize DISA Technologies’ mineral-processing test data for cloud analytics. Analyze historical records to assess data quality and identify machine-learning-ready relationships.