Top Senior Data Engineer Jobs

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

As a Senior Associate in Data Engineering at PwC, you will design and implement data architecture strategies, analyze complex issues, mentor junior members, and build client relationships while ensuring adherence to project standards.

The Senior Snowflake Data Engineer designs and optimizes Snowflake architecture and pipelines, ensuring data governance and performance across Marketing and Sales teams.

The Distinguished Software Data Engineer role involves driving data architecture and engineering solutions, mentoring teams, and influencing technology adoption to enhance Capital One's data strategy.

The Data Engineer will design, build, and manage data integrations and transformations, establish database standards, and troubleshoot complex issues while supporting production and analytics.

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

The Senior Data Engineer will operationalize data governance by developing tools for data discovery, maintain data quality, and implement enterprise initiatives.

Design and deliver enterprise-scale data models and analytics-ready datasets in Snowflake; build reliable ELT/ETL pipelines (batch and near-real-time); implement data quality, observability, and performance optimizations; publish curated data to downstream systems; enforce documentation, lineage, schema evolution, and secure handling of sensitive data while collaborating with product, operations, and engineering teams.

As a Data Engineer, you'll build and maintain data pipelines and infrastructure for an AI-driven research platform, ensuring data quality and reliability while collaborating on architectural decisions.

The Data Engineer will design and build scalable data systems, support AI/ML integration, and collaborate across teams for the development of Canoe's Alts Data Platform.

Lead design and implementation of a unified event-tracking data platform. Collaborate with Data, Product, and Engineering to build reusable event-publishing frameworks, monitoring/alerting, and deprecate legacy systems. Coach mid-level engineers, influence roadmap and processes, manage cloud/SaaS spend, create documentation, and participate in on-call rotations to ensure platform reliability and data trustworthiness.

Lead design and build of a scalable, secure cross-cloud data platform and marts. Drive selection and optimization of the modern data stack, enforce governance and lineage, and establish reusable data models and best practices to support analytics, BI, and ML use cases across product and engineering teams.

The Senior Data Engineer will architect, build, and maintain scalable data pipelines and infrastructure, mentor junior engineers, and collaborate on data solutions for Openly's insurance platform.

As a Staff Data Engineer, you develop data products, design data for analytics, and support Precision Ag business through data insights and tools. You will harmonize data, implement data pipelines, and lead teams on complex projects, ensuring efficient and impactful solutions for agriculture.

Lead design and implementation of scalable, HIPAA-compliant data pipelines and platforms for healthcare ML. Build ETL, orchestration, and tooling for processing EHR, claims, pharmacy, and bioinformatics data; collaborate with data scientists to produce modeling-ready datasets and ensure data quality, reliability, and operational excellence.

We're seeking a Data Engineer with 5+ years of experience to build data processing solutions using Python, design data models, and process large data streams while ensuring high performance across various data technologies.

Design and develop data models and pipelines, optimize data systems, collaborate with teams, enforce data governance, requiring 5-7 years of data engineering experience.

The Senior Data Engineer will design, develop, and implement technical solutions in full-stack development while collaborating with Agile teams. Responsibilities include utilizing programming languages, cloud services, and big data technologies to deliver robust solutions.

The role involves designing and optimizing data pipelines in Databricks, developing data assets, ensuring data quality, and collaborating with analytics teams to meet healthcare-related data needs.

Build and maintain data quality scripts and automated validation workflows, support large data migrations to modern platforms including public cloud, triage and repair data quality issues, prepare datasets for client-facing materials, and ensure access to databases for business intelligence tools.

Design, build, and maintain scalable data pipelines integrating diverse sources into analytics platforms. Own projects, standardize pipeline tooling, implement automated data tests, ensure security/PII handling, support on-call troubleshooting, and mentor team members to enable reliable, production-grade data delivery.

Design, develop, test, implement, and support cloud-based data solutions. Collaborate with Agile teams on full-stack and distributed systems, using Java/Scala/Python, RDBMS/NoSQL, data warehousing and streaming tools. Mentor engineers, perform unit tests and code reviews, and deliver performant, production-ready data platforms to support machine learning and customer-facing products.

Senior individual contributor leading design and build-out of enterprise Azure cloud data platform capabilities. Defines architecture, reusable patterns, and standards; provides hands-on implementation, technical mentorship, and cross-functional alignment to enable scalable, secure analytics and data solutions.

Lead design and implementation of enterprise Azure cloud data platform capabilities. Define reference architectures, reusable platform components, standards, and enablement for ingestion, storage, processing, security, and observability. Provide hands‑on engineering, technical leadership, cross‑functional collaboration, and mentorship to ensure scalable, secure, and consistent data solutions.