Top Data Engineer Jobs

Design, build, and optimize GCP-based data architectures and ETL/ELT pipelines using BigQuery, Dataflow, dbt, and orchestration tools. Ensure data quality, security, and integration from diverse sources; collaborate with stakeholders to deliver actionable insights.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Lead operational design and engineering for the data platform: own Snowflake and dbt administration, automate platform operations and CI/CD, monitor platform health and observability, enforce governance and access, optimize performance and costs, and develop AI/agent integrations to enable governed data access for business users.

Support design, development, and maintenance of data pipelines and infrastructure. Assist with ETL/ELT workflows, data ingestion, cleaning, validation, modeling, and warehousing. Document processes and collaborate with data teams to support analytics use cases.

Set up test, preproduction and developer environments on physical servers; act as SQL Server DBA including security auditing; develop Big Data solutions and programs; perform user acceptance and integration testing. Requires experience with DTS/SSIS/SSRS, T-SQL, C#, PowerShell, Hive/Hadoop, and Microsoft SQL Server 2000 certification.

Build and own the core data models and pipelines connecting product usage, accounts, revenue, billing, and customer health. Create trusted metrics, dashboards, and self-serve reporting. Partner with Product, Engineering, and GTM teams to improve data quality, design AI-enabled workflows and agents, and support leadership with accurate visibility into adoption, pipeline, retention, expansion, and revenue.

The Data Engineer will design and maintain data pipelines on Azure, manage ClickHouse for analytics, and ensure healthcare compliance while supporting ML integration.

Design, develop, and maintain batch and streaming data pipelines and ETL for large unstructured datasets. Collaborate with developers, data scientists, and analysts to build data management systems, containerize and deploy services (Docker/Kubernetes), and implement CI/CD, DevOps, and monitoring on Linux/AWS infrastructure for intelligence community missions.

Design, build, and maintain Data Vault 2.0 data warehouse and Power BI solutions. Develop ETL pipelines, data models, reusable datasets, and data governance. Perform profiling, cleansing, migrations, and collaborate with infrastructure teams to optimize data delivery, automation, and reporting.

The Data Engineer will construct data infrastructure, develop machine learning workflows, maintain data pipelines, integrate software components, and collaborate across teams to enable data-driven decisions.

Design, build, and maintain scalable relational databases, data warehouses, and integration workflows. Optimize SQL performance, ensure data integrity/security, support reporting/analytics, and collaborate with business stakeholders for timely, compliant reporting and continuity efforts.

Design, develop, and maintain ELT/ETL Snowflake data warehouse solutions; implement data standards, CI/CD, monitoring, and production support; collaborate with product, business, and vendors; mentor junior developers and improve data engineering practices.

Lead end-to-end data integrations with utility customers: scope requirements, map and transform datasets, run and monitor pipelines, triage issues, and productize repeatable tooling to speed implementations.

Design, build, test, and operationalize data pipelines and solutions on cloud and Databricks. Ensure data quality, governance, security, and support incident resolution. Collaborate cross-functionally to deliver analytics, ML/AI enablement, and educate users on data products.

Own and maintain the companys data infrastructure and pipelines. Design, build, and monitor ETL/ELT workflows and data architecture; integrate SaaS sources; enforce data quality, standards, and governance; partner with stakeholders to deliver analytics-ready data and meet data availability SLAs.

Hands-on technical lead for global Azure-based data analytics environments. Own escalation support for complex incidents, performance/scalability troubleshooting on large datasets and pipelines, guide support analysts, and partner with engineering to drive durable fixes across Databricks, SQL Server/DWH, and Azure data services.

Design, build, and maintain scalable data pipelines and platforms using Python, PySpark, SQL, and Databricks. Integrate and validate data from multiple sources (APIs, structured/semi-structured) to support analytics, operational planning, and decision-making. Lead cross-disciplinary teams, ensure data quality and performance, and communicate technical findings to stakeholders on mission-focused DoD projects.

Owner of data platform modernization: design and migrate legacy systems to Kimball-style dimensional models and star schemas; build reliable production data pipelines; enforce data hygiene and validation; champion dbt best practices; mentor junior engineers; collaborate with analysts and stakeholders to ensure scalable, well-documented data models and platform observability.

Design, build, and maintain scalable cloud-native data pipelines, microservices, and platforms to support real-time and batch processing. Collaborate with architects and stakeholders to translate requirements, implement AWS-based ingestion/streaming solutions, enforce data quality/testing, and operate observability and CI/CD practices to ensure reliable production data systems for autonomy initiatives.

Design, build, and maintain scalable data pipelines, microservices, and cloud-native data platforms. Implement real-time and batch processing using Python/Java and AWS services, ensure data quality and automated testing, monitor production systems, collaborate with architects and stakeholders, and drive operational excellence and CI/CD deployment for large-scale distributed data environments.

Lead and mentor a data engineering team to design, build, test, and support cloud-based, real-time data platforms and distributed microservices using modern big data, streaming, and data warehousing technologies to deliver scalable customer and enterprise experiences.

Lead Data Engineer responsible for designing data models, implementing data solutions, managing data pipelines, and collaborating with teams to drive technical initiatives.

Lead design, build, test, and support large-scale data pipelines and cloud-based solutions. Collaborate with Agile teams, mentor engineers, optimize performance, and work with ML, streaming, and data warehouse technologies to drive customer acquisition and analytics at scale.

Lead design, develop, test, deploy and support cloud-based data engineering solutions. Work across Agile teams on distributed, real-time and batch data platforms, mentor engineers, and optimize code and data pipelines using big data, streaming, and cloud data warehousing technologies.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.