Top Senior Data Engineer Jobs

The Data Engineer will develop data pipelines, manage complex data sets, and ensure data reliability while collaborating within an Agile/Scrum team.

The Data Engineer develops data pipelines, supports Agile teams, ensures data performance, advocates for lean practices, and collaborates across functions to deliver insights for federal customers.

Responsible for developing data pipelines, ensuring data extraction performance, and collaborating in an Agile team to provide data-driven solutions.

This role involves developing data pipelines, ensuring data performance, automating processes, and collaborating within Agile teams to meet complex data requirements.

As a Sr Data Engineer, you'll conduct feasibility analyses, develop applications, monitor processes, consult with users, and ensure compliance while serving as a mentor to junior analysts.

Build and operate real-time and batch data pipelines for maritime domain awareness. Parse binary sensor and SONAR data, design high-performance data warehouses, validate and normalize sources, and improve local development and CI/CD. Work on-site with customers, write production-quality code, and integrate systems to support mission-critical deployments.

The Data Engineer is responsible for designing data workflows and pipelines, implementing systems engineering principles, and deploying data streaming technologies while ensuring compliance with industry regulations and best practices.

Forward-deployed senior data engineer who deploys, hardens, integrates, and operates a data platform (lakehouse, streaming, governance) in DoD environments. Build pipelines across classification boundaries, produce accreditation evidence (STIG/cATO/FIPS), run day-2 operations, train mission teams, and mentor junior FDEs while feeding field feedback into product improvements.

Develop and maintain data infrastructure for safety data analysis, build scalable data pipelines, conduct exploratory analyses, and support regulatory reporting in AV safety.

Design, implement, and optimize scalable data pipelines and infrastructure on AWS. Maintain ETL artifacts, provide Tier 3 support, collaborate with stakeholders and a product team, document dataflows, and occasionally support off-hours deployments.

As an Analytics Engineer, you will create scalable solutions for data reporting, build data pipelines, and establish high data integrity standards while collaborating with multiple teams.

As a Snowflake Data Engineer, you will design and maintain scalable data pipelines, optimize SQL transformations, and support analytics platforms, collaborating with stakeholders.

Design and maintain scalable ETL/ELT pipelines, data integrations, APIs, databases, warehouses, and workflow orchestration. Improve data quality, security, governance, reporting accessibility, and infrastructure reliability across a multi-location organization. Collaborate with analysts, technology teams, and business leaders, troubleshoot complex data issues, support CI/CD deployments, and provide technical leadership and mentorship while shaping long-term data strategy.

Design, build, and maintain scalable ETL/ELT pipelines and data platforms. Lead cross-functional teams to ingest, parse, and process structured and unstructured data, develop scripts for data conversion, support operations and monitoring of secure information systems, and apply version control and agile practices to deliver analytics-ready data for mission-driven clients.

Design, build, test, and operationalize complex data pipelines and multi-layer data architectures using Snowflake and dbt. Analyze source systems, perform data mapping and transformations, ensure data governance, quality, and security, resolve defects, optimize query and pipeline performance, and collaborate with engineering and business teams to support BI and AI/ML use cases.

Lead end-to-end data architecture and engineering for scalable AI and analytics systems. Define standards for data modeling, ingestion, transformation, quality, governance, security, and observability. Mentor data engineers, lead complex projects, collaborate with AI/ML and software teams, translate stakeholder needs into technical designs, and drive data-driven innovation across the organization.

Lead the architecture and technical standards for Curaleaf’s data and analytics ecosystem. Design secure, scalable Snowflake environments; establish SQL, Python, dbt, modeling, testing, CI/CD, and governance practices; and develop reusable data products and platform capabilities. Partner with engineering, analytics, data science, governance, and business teams, guide technical roadmaps, optimize performance and costs, lead incident reviews, and mentor engineers through cross-team technical influence without direct people management.

Designs, builds, and operates scalable data platforms and ELT pipelines across Google Cloud using dbt, Python, Airflow, and BigQuery. Responsibilities include data modeling, governance, security, cost optimization, observability, infrastructure as code, incident response, and end-to-end feature delivery. The role pilots AI-assisted engineering practices, develops reusable tooling, collaborates with business and technical stakeholders, and mentors data engineers.

Design, build, and lead a scalable, configuration-driven data integration platform. Improve performance, automate monitoring and deployments, provide escalated support, and collaborate across product and engineering to deliver reliable, secure data integrations.

Design and build scalable pipelines and evaluation frameworks to compute driving-performance metrics across fleet and simulation data. Perform deep analysis of time-series, event, and geospatial data to identify regressions and root causes. Build geospatial analytics, release gating tooling, and LLM-powered agentic workflows to triage issues and propose fixes. Collaborate with engineering teams and adhere to QMS processes.

Design, build, and operate scalable batch and streaming data pipelines, orchestration workflows, governed datasets, and data products. Develop data models and analytics foundations supporting BI, AI, ML, and GenAI use cases. Partner with product, analytics, data science, AI, and business teams to translate requirements into production-ready solutions. Monitor and improve reliability, performance, quality, security, observability, and operational support across data and ML workflows.

The Lead Data Engineer will design and build data architectures, lead a team, and develop ETL processes using cloud technologies to optimize data solutions for the U.S. business.

Design, develop, and maintain scalable data pipelines and models in Databricks and Microsoft Azure. Perform data ingestion, transformation, integration, migration, and modernization across enterprise data sources. Monitor and optimize pipeline performance, reliability, and data quality. Translate business requirements into technical solutions, collaborate with architects and engineers, follow security and governance standards, participate in code reviews, and document data platforms and processes.

Build and maintain scalable data pipelines, analytics data warehouses, ETL processes, data models, and infrastructure supporting reporting, analytics, and machine learning. Ensure data quality from creation through presentation layers, translate stakeholder needs into deliverables, improve tooling and pipeline reliability, and participate in on-call support. The role involves modern big data architecture, containerization, cloud platforms, and data engineering technologies.

Designs and scales streaming and batch data pipelines supporting AI applications and advertising technology. Builds embedding workflows, vector-store integrations, retrieval systems, AI-ready data models, and reusable ingestion components. Ensures observability, data quality, schema governance, reliability, recoverability, and auditability. Partners with AI, infrastructure, product, and governance teams to deliver production-ready systems, leads technical execution, mentors junior engineers, and influences architecture.