Top Senior Data Engineer Jobs

Lead design, build, and maintain enterprise-scale data platforms and pipelines. Mentor cross-functional teams, develop software to process structured/unstructured data, and operationalize ETL/ELT workflows for analytics in agile environments supporting mission-driven projects.

Designs and owns analytics-ready datasets, semantic views, ELT pipelines, and standardized metrics in Snowflake. Partners with business, analytics, and engineering teams to enable self-service BI, maintain data quality and documentation, and support forecasting, machine learning, and generative AI initiatives. Provides senior guidance on data modeling, analytics engineering practices, and scalable data product design.

Build and maintain data pipelines, models, automations, applications, and analytics solutions on Celonis and enterprise data platforms. Collaborate with stakeholders, process analysts, data scientists, and domain experts to translate business needs into scalable solutions. Ensure data quality, governance, interoperability, performance, reliability, and ongoing application support across systems such as SAP, Salesforce, and Databricks.

Designs and maintains data pipelines, ETL processes, automation solutions, integrations, and data-processing utilities. The role transforms disconnected operational data into secure, structured, AI-ready systems using Python, SQL, Microsoft Power Platform, SharePoint, Power BI, Tableau, APIs, and Azure services. Responsibilities include building dashboards, organizing knowledge-management structures, supporting RAG systems, and independently managing solutions through the full software lifecycle while collaborating with government stakeholders.

Design, build, and maintain scalable data pipelines, lakehouse/data‑warehouse architectures, and ETL/ELT workflows to support analytics and AI/ML. Ensure data quality, lineage, monitoring, and SLA-driven reliability; support project delivery, client communication, and business development under project management guidance.

Design, build, and own a scalable, HIPAA-compliant data management system that fetches partner data (e.g., EPIC), extracts semantic knowledge, summarizes provider notes, and provides user-facing UI for viewing and updating data to power provider matching, scheduling, and recommendation products.

Design and operate Alpaca’s scalable data platform on GCP, including lakehouse infrastructure, Kubernetes-based deployments, streaming and CDC pipelines, batch ingestion, BI access, cataloging, monitoring, and governance. Build infrastructure as code with Terraform and Ansible, operate distributed query engines and Apache Iceberg, maintain reliability practices, and collaborate with DevOps and analytics teams to support rapidly evolving data requirements.

Build, scale, and operate an enterprise telemetry data platform that ingests, processes, and publishes vehicle telemetry at global scale. Develop batch and streaming pipelines using Databricks and Spark, implement observability and data quality controls, optimize distributed workloads for performance and cost, and enable reliable production data products through CI/CD and Infrastructure as Code. Provide technical leadership and partner with analytics, AI, and engineering teams.

Design and implement scalable data models and ETL pipelines (batch and streaming), ensure data quality/SSOT, collaborate with product and platform teams to resolve issues, contribute to data governance, and explore practical integrations between data engineering and AI.

Build and operate scalable data pipelines and services to power internal products. Design data models, curate high-quality datasets, collaborate with cross-functional teams, and mentor engineers while improving platform reliability, observability, and AI-ready data layers.

Build and lead Way’s data infrastructure from the ground up. Design cloud-native data lakes, scalable batch and real-time ETL/ELT pipelines, ingestion frameworks, data warehouses, orchestration, streaming systems, data quality monitoring, and DataOps practices. Develop Python and SQL-based data solutions, infrastructure as code, feature stores, dashboards, and self-service analytics. Partner with product and business teams on metrics, predictive modeling, and experimentation while establishing data-driven practices.

Develops and administers complex ETL and ELT data integration solutions using Microsoft Fabric, SQL Server, Lakehouses, Warehouses, pipelines, notebooks, and SSIS. Designs reliable data flows, consumption views, semantic datasets, and enterprise data models; troubleshoots data quality and pipeline issues; supports database administration, backup and recovery, reporting, deployment, and CI/CD. Collaborates with business, analytics, technical, vendor, and reporting teams to deliver secure, scalable, trusted data products.

Provide data engineering and analysis consulting to a Department of Defense client. Responsibilities include data modeling, data integration, software development, collaboration with clients and technical teams, and delivery of high-quality data solutions. The role requires an active Secret clearance, a bachelor’s degree, and at least eight years of relevant experience.

Design, build, and operate scalable cloud-native data platforms and pipelines supporting analytics, reporting, AI, and customer-facing applications. Develop ETL/ELT workflows, data lakes, event-driven streaming solutions, integrations, and data models using Python, SQL, AWS, databases, warehouses, messaging technologies, and orchestration tools. Monitor production systems, optimize performance, automate testing and deployment, and collaborate with engineering, platform, product, and data teams.

Design, build, and operate scalable cloud-native data platforms and pipelines supporting analytics, reporting, AI workloads, and customer-facing applications. Develop ETL/ELT processes, data lakes, event-driven and streaming solutions, data models, enterprise integrations, and automated monitoring and deployment systems. Collaborate with software, platform, product, and data teams to ensure reliable, secure, high-performance production systems.

Architects and operates petabyte-scale telemetry ingestion, processing, and storage systems. Builds resilient distributed systems and data pipelines using streaming or batch frameworks in Kubernetes. Establishes observability with ELK and Grafana, collaborates across engineering teams, and applies AI-assisted tools to improve development, security, validation, and operational reliability.

Designs and maintains scalable data pipelines, develops dbt transformation workflows, and architects Snowflake data models and warehousing solutions. Integrates multiple data sources, implements quality monitoring, troubleshoots pipeline issues, optimizes SQL and warehouse performance, documents architecture, and collaborates with analysts, data scientists, and stakeholders to deliver reliable analytics infrastructure.

Develop and maintain data integration, transformation, modeling, and database processes across Azure SQL Database and Azure Cosmos DB. Diagnose data issues, optimize data structures, support CI/CD and production operations, and establish data standards and documentation. Collaborate with business and technical stakeholders to define requirements, service levels, and quality expectations within an Agile development environment.

Designs and builds production data systems, pipelines, database schemas, analytical models, and internal platforms. Responsibilities include writing tested code, optimizing queries and storage, controlling infrastructure costs, enforcing CI/CD, and implementing data testing, monitoring, and alerting. The role also develops AI and LLM-backed data use cases, including retrieval pipelines, embeddings, vector storage, evaluation harnesses, and agentic workflows.

Build and maintain ETL and data pipelines using Python, PySpark, and AWS services. Orchestrate workflows, develop event-driven integrations, optimize data storage and queries, process API and JSON data, and implement data quality monitoring. Support CI/CD, production operations, cloud migration, data lake and lakehouse initiatives, and near-real-time systems while collaborating with stakeholders to deliver technical solutions.

As a Data Engineer, you will design and maintain data pipelines, support DoD systems, and ensure data quality and compliance.

Design, build, and maintain scalable data pipelines and architectures. Perform data extraction, cleansing, transformation, and ensure data quality and accessibility. Collaborate with data scientists, analysts, and product teams to deliver data solutions and troubleshoot data issues.

Design, build, and maintain scalable ELT pipelines and data models for insurance (Guidewire) domains and Generative AI. Ensure data quality, performance, governance, and security; support analytics, AI model training (embeddings, vector DBs), dashboards, testing, and mentoring.

Design, build, and maintain scalable data pipelines, lakes, and infrastructure using Databricks, Spark, Python, and AWS. Collaborate with ML engineers, data scientists, and analysts to deliver data products, ensure data quality and governance, implement DevOps/SDLC practices, mentor junior engineers, and evaluate new data technologies.

Architect, build, and maintain Snowflake-based ELT pipelines and production data models. Implement observability, data quality, and governance; optimize cost and freshness. Partner with Analytics, Product, and Engineering, use dbt/CI-CD and AI-assisted tooling, and participate in reviews and architecture planning to support analytics, BI, and ML.