Top Data Engineer Jobs

Lead architecture and scaling of the Databricks data warehouse and self-service data platform. Build reusable batch and streaming frameworks, enforce governance, SLOs, lineage, observability, CI/CD, and multi-tenancy. Mentor engineers, run design reviews, and ship documented patterns to enable analytics, data science, and app teams to move quickly without blocking on platform tickets.

The Senior Data Engineer designs and develops scalable data architectures across platforms, mentors team members, and enhances data engineering capabilities for insights and decision-making.

Lead architecture and modernization of the enterprise data platform. Build scalable serverless data pipelines with Airflow, dbt, Python, and AWS. Integrate Fivetran, Redshift, and Sigma, implement observability, enforce data governance, and mentor engineers to improve reliability and analytics deliverables.

Design, build, and maintain scalable Microsoft Fabric pipelines and lakehouse models, optimize Power BI semantic models and refresh strategies, enforce engineering/SDLC best practices (Git, CI/CD, testing), drive data modeling standards, mentor teammates, communicate technical decisions to non-technical stakeholders, and ensure SLA reliability and observability for client-facing analytics.

Lead data engineering work across migrations, pipelines, integrations, and APIs. Design, build, deploy, test, monitor, and support production data solutions while ensuring data quality, validation, and adherence to architectural standards in an agile continuous-delivery environment.

Design, build, and maintain backend systems and data interactions for healthcare reporting. Develop APIs, data models, ETL pipelines, and interfaces; optimize performance, ensure data integrity and security, and support automated testing and CI/CD. Collaborate in Agile teams and reduce technical debt while enabling integrations with frontend and third-party systems.

Lead modernization of the enterprise data platform and build scalable ETL/ELT pipelines using SQL, Python, Talend, and Databricks. Drive migration from DB2/SQL Server to Databricks, set data quality, observability, and lineage standards, design data models, contribute to CI/CD and IaC practices, and mentor junior engineers while partnering with analytics, product, and business stakeholders.

Design, build, and maintain scalable, fault-tolerant identity and device data pipelines and backend systems. Modernize data models, improve code quality and test coverage, support production operations and on-call, collaborate with product and cross-functional teams, and participate in code reviews and agile ceremonies.

Build end-to-end data and analytics features: ingest and model data, build transformations and pipelines, create client-facing dashboards and interfaces, and develop LLM-powered workflows to accelerate analysis and app development. Partner with stakeholders, perform exploratory analyses, and ensure data quality, observability, and maintainability while influencing data engineering strategy.

Lead design, build, and maintain scalable batch and streaming data pipelines and data warehouse models. Ensure data quality, observability, performance, and security. Own workstreams, support production incidents, and enable analysts and downstream applications.

Design, build, and optimize cloud data platforms using Azure, Databricks, and Fabric. Lead discovery, architecture, and data ingestion (batch/streaming), implement medallion/lakehouse designs, engineering transformations, and recommend governance, DevOps, and security best practices while working directly with clients.

Administer, stabilize, and scale Oura's global Databricks environment. Manage workspace governance, cluster policies, access control, cost monitoring, job scheduling, and incident SLAs. Onboard teams, resolve performance and reliability issues, and partner with IT and Data Architecture. Over time contribute to pipeline development, Spark optimization, CI/CD, and infrastructure-as-code to support domain-oriented data mesh initiatives.

Design, build, and maintain scalable data products and data warehouses to measure content performance and subscriber movement. Develop ETL pipelines in cloud and big-data environments, define data models, implement automated data quality checks, tune SQL performance, document solutions, meet SLAs, and collaborate with product, analytics, and infrastructure teams using agile practices.

Design, build, and maintain Identity Householding data products and scalable batch/real-time pipelines. Improve data models and code quality, increase test coverage, collaborate with product and engineering teams, participate in on-call support, code reviews, and CI/CD-driven deployments using Databricks, Spark, cloud databases, Airflow, and AWS S3.

Design, implement, and support enterprise data integrations connecting JDE, Workday, Salesforce and other platforms. Lead SQL-driven ETL/ELT pipelines, develop reusable integration frameworks, ensure data integrity and audit readiness, perform regression and production troubleshooting, and provide architectural oversight and mentorship across multiple concurrent projects.

As a Senior Data Engineer, you will design data pipelines, collaborate with teams, build demos, and improve data processes within a cloud-native environment.

Design, implement, and deliver big-data marketing solutions and ETL/integration frameworks. Lead customer engagements, perform data modeling and cleaning, create technical documentation, conduct code reviews, testing, and deployments while collaborating with cross-functional teams.

Design, develop, and implement data integration and ETL solutions connecting disparate systems. Monitor, troubleshoot, and support production incidents; maintain documentation; collaborate with cross-functional teams and apply healthcare data standards to improve clinical and operational data flows.

The Senior Data Engineer will build and deploy data pipelines, develop warehouse architectures, and design analytics solutions for the team.

Design and own end-to-end data domains and pipelines for plant sensors, lab results, and historians. Build schemas, ensure data quality, observability, and lineage, and supply model-ready time-series and analytical datasets to ML and operations teams. Mentor junior engineers and define data contracts.

As a Data Analytics Engineer, you will build data pipelines, develop KPIs, collaborate with stakeholders, and promote data-driven decision-making.

The Senior Data Engineer will design and manage petabyte-scale data pipelines, ensuring data quality and architecture for operational decision-making and ML training.

The Principal Database Architect & Engineer will own the data components of complex infrastructure solutions and lead teams to deliver innovative database technology solutions, ensuring optimal performance and operational efficiency.

The Senior Data Engineer will manage data tuning on relational databases, utilize Big Data technologies, and coordinate with technical teams and business users.

The Senior Data Engineer will design and manage data pipelines for massive datasets, ensuring quality, cost management, and effective data operations. Responsibilities include overseeing data flows, monitoring data quality, and managing vendor partnerships.