Top Data Engineer Jobs

As the first in-house Data Engineer, you will own the data infrastructure, build and maintain data pipelines, and collaborate with teams to ensure quality data access and insights.

Lead architecture and evolution of Headway's data platform (warehouse, ingestion, orchestration, CI/CD, monitoring, cloud infra). Serve as technical anchor across analytics, product, and ML teams, drive platform roadmaps, set standards, mentor engineers, and own end-to-end infrastructure decisions for scale and performance.

Lead design, build, test, and support cloud-based data solutions. Collaborate across Agile teams on full-stack and distributed systems, mentor engineers, implement big data and streaming pipelines, and ensure code quality and performance for production data platforms.

Build and maintain data infrastructure to measure agent behavior and user interactions, design evaluation frameworks for AI agents, analyze user behavior to inform product strategy, and partner with engineering and leadership to solve high-impact business problems using data.

Design, build, and maintain enterprise-scale data platforms and ETL/ELT pipelines. Develop software and scripts to ingest, parse, and process structured and unstructured data. Collaborate with cross-functional teams to scale, monitor, and operate data solutions using Databricks, Spark, and CI/CD practices to support mission-driven analytics.

The AWS Data Engineer designs and maintains data solutions on AWS, implementing scalable pipelines, integrating systems, ensuring security, and optimizing workflows.

The Workday/Fabric Data Engineer will develop and maintain business intelligence initiatives, including reporting, data pipelines, and data warehousing to support strategic objectives at USNH.

The Data Engineer will design, build, and maintain ETL/ELT pipelines, contribute to data infrastructure, and collaborate with teams to ensure data quality and governance.

As an Analytics Engineer, you'll manage the data stack, design database architecture, build data pipelines, and oversee reporting and data quality.

The Data Engineer will design and maintain ETL pipelines on Databricks, ensuring data quality and performance for banking applications, while mentoring junior engineers.

The Lead Cloud Data Engineer will design and optimize ETL pipelines, manage data quality, and collaborate with teams to meet data needs. Responsibilities include monitoring pipelines and mentoring junior engineers, while leveraging AWS and other cloud technologies.

The Data Engineer will execute data architecture projects, manage data ingestion processes, ensure data quality, and transition data to new systems while collaborating with investment teams.

Design, build, and maintain scalable data ingestion and ETL pipelines across a growing data lake. Define and enforce data quality standards, SLOs, and validation frameworks. Optimize pipelines for performance and cost, expand monitoring and alerting, and enable stakeholders to self-serve on data infrastructure.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

Senior Addepar data engineer/analyst responsible for supporting and scaling Addepar integrations, building and maintaining data pipelines (Addepar↔Snowflake/Canoe/other platforms), diagnosing and resolving data quality issues, developing SQL-based reconciliations and automation, partnering with business and technology stakeholders, and supporting production operations, governance, testing, and documentation for wealth and asset management data.

Build and maintain scalable data pipelines, trusted datasets, and reporting systems to support infrastructure deployment, hardware operations, capacity planning, and supply chain analytics. Partner with stakeholders, improve data quality, lineage, and observability, and enable executive reporting, forecasting, and operational reviews. Integrate new telemetry sources and document systems for long-term maintainability.

Develop and maintain Python-based data applications and pipelines: fetch data from databases/APIs/files, build Pandas DataFrames/NumPy arrays, write complex SQL and data transformations, handle nested JSON, and collaborate with data science teams to deliver production data platforms.

Builds and optimizes ETL/ELT pipelines, integrates disparate data sources, ensures data quality/security, supports analysts and data scientists, and implements data governance and metadata management.

Lead design, build, and operationalization of a scalable enterprise data platform and pipelines (batch and streaming) to support analytics and AI. Drive architecture for lakehouse, ETL/ELT, CDC ingestion, observability, governance, and mentor engineers while collaborating with cloud, MDM, and product teams.

Support DaaS/Digital Marketplace deployments and data set publishing across Google Marketplace, AWS, and Snowflake. Automate uploads/refreshes, QC steps, and product configurations; troubleshoot integrations; create buckets/views/shares; develop repeatable automation frameworks; collaborate in an Agile team and manage multiple tasks independently to ensure timely, accurate delivery of data pipelines and product data sets.

Design, build, and maintain Databricks-based ETL/ELT pipelines and medallion (bronze/silver/gold) lakehouse architectures. Ensure data quality, lineage, governance, and performance for large federal health datasets. Collaborate with data scientists, analysts, and CMS stakeholders and support Agile development and CI/CD workflows.

As a Lead Data Engineer, you will design and develop solutions, collaborate with teams on data technologies, and support cloud-based data warehousing and big data applications.

The Lead Data Engineer will design and develop technical solutions, collaborate with agile teams, and implement cloud-based data solutions, focusing on real-time data applications and big data technologies.

As a Senior Data Engineer at Jellyfish, you'll build and maintain data pipelines, optimize orchestration, automate CI/CD processes, and enhance data integration while ensuring high performance and reliability.

Designs and builds scalable distributed data processing systems and data warehouse solutions on-prem and in AWS. Develops ETL/ELT pipelines, optimizes databases, implements CI/CD, mentors team members, liaises with business units, and ensures performance, reliability, security, and documentation for BI and analytics applications.