Top Data Engineer Jobs

Build and optimize high-volume data processing jobs and libraries (Redshift, Athena, Iceberg, Spark, Airflow). Debug and improve query and job performance, and collaborate with data scientists, analysts, and product managers to support analytics and data products.

Work at the intersection of software engineering and operations to investigate production issues, improve reliability, build automation, enhance observability, and support customers. Develop and maintain C# and SQL Server solutions, reduce manual effort, convert production learnings into lasting improvements, and provide occasional after-hours support.

Own end-to-end consumer data domains: ingest, clean, model, and productionize derived attributes and pipelines. Investigate new data sources, ensure quality and provenance, and partner with engineers, data scientists and product teams to deliver trustworthy data products that power applications, agents, APIs and ML models.

Build, maintain, and optimize cloud-based data pipelines and ETL processes. Collaborate with business stakeholders to design data models and deliver scalable warehousing solutions. Implement data quality checks, monitor performance, document systems, and enable self-service analytics (Power BI). Support migration from legacy systems and drive data engineering best practices across environments.

Maintain and monitor data pipelines that aggregate healthcare claims for the Cost Roll process. Validate and reconcile claim and provider demographic data, troubleshoot data issues, perform data quality checks, and collaborate with stakeholders to ensure reliable downstream financial and clinical systems.

Build, run, and maintain end-to-end data pipelines for mass spectrometry and molecular datasets. Ingest, convert, label, and enrich messy scientific data; implement data quality checks, monitoring, tests, and reproducible builds for ML training and product-read datasets.

Contribute to building and maintaining cloud-based data pipelines and data products. Develop, test, and optimize SQL and Python transformations, participate in code reviews, troubleshoot data quality issues, and follow CI/CD and documentation practices to support analytics and AI initiatives.

Design, build, and maintain automated ETL/ELT pipelines to integrate enterprise systems, ensure data quality, and deliver curated business-ready datasets. Develop Python-based data processing, document architectures and workflows, support Palantir and ERP platforms, and collaborate with stakeholders to define requirements and scalable solutions.

Build and maintain Snowflake and dbt data platforms, including ELT pipelines, tested models, orchestration, monitoring, documentation, access controls, and governance. Optimize performance and costs while ensuring reliable, discoverable data for internal dashboards, client products, and AI/ML use cases. Translate ambiguous stakeholder needs into durable solutions and apply strong software engineering practices, including Git, CI/CD, code review, Python, testing, and alerting.

Design and maintain pipelines and tooling to ingest, validate, model, and serve multi-modal driving data for ML training, evaluation, and debugging. Collaborate with ML, vehicle software, curation, and fleet teams to scale the data stack and make datasets discoverable and queryable.

Design, build, and maintain data pipelines, ETL processes, and data warehousing solutions using Python and GCP/BigQuery. Ensure data quality, optimize storage and processing, support new data products, and mentor junior engineers while collaborating with the CTO and team.

Design, develop, and maintain scalable data pipelines, ETL processes, and BigQuery/GCP-based data storage. Ensure data quality, implement monitoring, assist product launches, mentor junior engineers, and collaborate closely with the CTO and team.

As a Data Engineer, you will build and optimize data pipelines, ensuring accurate data delivery that informs pricing and market analytics at Alt.

The Data Engineer will develop and implement software solutions for law enforcement, focusing on data management, system optimization, and effective communication with clients.

The Data Engineer will develop data and software solutions for law enforcement, create reports, and manage data sources. Responsibilities include collaborating with teams to analyze data, communicate with clients, and ensure high-performance analytics tools.

As an Azure Data Platform Consultant, you will deliver data solutions using Azure and Fabric, engage with clients, design solutions, and optimize data platforms.

Design and maintain scalable batch and real-time data pipelines, build governed data models for a CDP, ensure data quality and security, support identity resolution and customer 360, mentor junior engineers, and collaborate with analytics and business stakeholders to deliver actionable customer insights.

Build, maintain, and support scalable data systems and pipelines. Ingest, clean, structure, and enrich data from multiple sources. Ensure data quality, security, and governance. Collaborate with analysts and data scientists to design solutions, deploy and optimize pipelines, and diagnose and resolve data issues to support analytics and reporting.

The role involves engineering modern data platforms, focusing on data ingestion, transformation, and storage while utilizing Azure services and MS Fabric, supporting an investment management firm.

The Data Engineer will lead data lifecycle strategy, manage data acquisition and cleansing, and collaborate with teams to optimize data solutions and architecture.

Design and build production-scale data pipelines, orchestration, and integrations on GCP to enable metadata-driven governance, lineage, automation, and AI-enabled workflows. Implement secure, observable, cost-efficient cloud data solutions, embed governance into engineering workflows, and drive CI/CD, testing, monitoring, and platform engineering best practices.

Design, build, and maintain scalable ETL/ELT pipelines (batch and streaming); manage data catalogs and API integrations with OAuth2; collaborate with cross-functional teams to ensure data quality and platform reliability for mission-driven projects.

Design and build scalable backend services and data ingestion pipelines, perform data modeling and large-file processing, develop reusable APIs, and optimize systems for speed and scalability while collaborating in an Agile team.

Design, build, and maintain core data infrastructure and automated end-to-end pipelines to ingest, process, store, and transform data. Develop data models and schemas, implement processing frameworks, enforce data quality and lineage, troubleshoot pipelines, collaborate with data scientists and security teams, document architecture, and support data governance and continuous improvement.

Builds and manages enterprise data pipelines, transformations, reporting structures, and analytics infrastructure using operational data from Oracle Fusion, SAP, and related platforms. Supports secure, accurate financial, HR, and procurement reporting through Microsoft Fabric and Power BI, including real-time dashboards and historical analysis.