Top Data & Analytics Jobs

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.

The Senior Data Engineer will scale and evolve the data platform, develop ETL/ELT pipelines, establish best practices, and optimize systems for performance while collaborating with various teams.

The Sr. Data Engineer - Databricks designs and maintains scalable data solutions using Apache Spark and Databricks, ensuring data quality and performance for analytics and reporting.

Responsible for designing, building, and maintaining real-time data ingestion pipelines, ensuring reliable data flow and quality for operational decisions.

Lead modernization and operation of Dutch Bros' data platform: design, build, and maintain scalable Snowflake-based data pipelines and infrastructure, enable real-time ETL/ELT, support analytics and ML workflows, implement CI/CD and IaC, ensure data governance and security, and communicate technical solutions to stakeholders.

Design and optimize the enterprise data warehouse and ELT pipelines (batch and real‑time). Integrate Databricks, CRM, and external systems into a governed, observable data platform. Drive automation, cost optimization, and monitoring; mentor junior engineers; and partner with Analytics, Ticketing, and Marketing to deliver scalable data solutions that support reporting and ML initiatives.

Responsible for utilizing Hortonworks architecture and tools such as Hadoop and Spark for data analysis and engineering tasks. Candidates should have relevant experience and may be considered for contract-to-hire roles.

The Sr. Ab Initio Data Integration Engineer will maintain ETL processes, troubleshoot performance issues, and provide documentation for the data warehouse environment.

Build and maintain automated ETL/ELT data pipelines, improve data quality and monitoring, optimize Postgres and BigQuery schemas and queries, develop internal tooling, support data governance (HIPAA), participate in on-call rotation, and collaborate across engineering teams to scale data systems.

The role involves building scalable data platforms, developing data pipelines, mentoring engineers, and enhancing data security while collaborating across teams.

Lead design, build, and maintenance of scalable data pipelines and lakehouse infrastructure using PySpark, Databricks, and Airflow. Own data & ML platform (Terraform, Databricks), improve observability, data quality, and developer tooling, and partner closely with Data Science and engineering teams.

As a Data Engineer, you will architect and maintain the data systems, create pipelines and integrations, and ensure data quality for analytics and reporting.

As a Data Operations Engineer, you will manage partner data lifecycle, ensuring data quality and performance while collaborating with teams to enhance data integration processes and guide architecture decisions.

Implement and maintain ETL processes to ingest and transform hospital data into Meduit platforms. Build data models, ensure data integrity, document and track tasks in Jira, collaborate with cross-functional teams, support revenue-cycle data implementations, and mentor team members on best practices.

Lead design, operation, and reliability of production data pipelines in a multi-terabyte Snowflake environment. Translate client requirements into dimensional models, debug and remediate pipeline failures, evolve the data mart for event-driven and AI workflows, and improve pipeline trustworthiness via testing, CI/CD, audit logging, and documentation.

As a Senior Data Engineer, you will build and optimize data pipelines, integrate AI capabilities, and collaborate across teams to create data-driven solutions, while providing technical leadership and ensuring data quality and governance.

The Senior Data Engineer designs and optimizes scalable data pipelines using Azure services, collaborates with teams, mentors others, ensures data quality, and drives innovation in data architecture.

Design, build, and maintain scalable batch and ELT/ETL data pipelines and services. Develop and operate Spark and Airflow workflows, apply software engineering practices, contribute to AWS-based systems, monitor and automate production, collaborate with product and data science teams, and mentor other engineers.

The Senior Data Engineer will design and maintain data pipelines, write production-grade Python and SQL code, and support AI initiatives while collaborating across teams.

Lead architecture and implementation of cloud-native data platforms on Google Cloud (BigQuery). Build unified batch/streaming pipelines, enforce IaC with Terraform, implement open table formats and governance (Dataplex/Data Catalog), automate data quality/lineage, partner with analytics and product teams, and mentor data engineers.

The Principal Data Platform Engineer leads the architecture and delivery of data platforms using Databricks, focusing on data pipeline design and client relationships.

The Senior Data Quality Specialist will develop data quality rules, implement cloud-based solutions, and analyze data accuracy, leading projects and collaborating with teams.

Design and maintain scalable data pipelines using Snowflake, optimize performance, collaborate with teams for robust data solutions, and enforce data quality checks.

Design, build, and maintain scalable data pipelines, warehouse architecture, and BI solutions. Lead data modeling, integration (CDC/streaming), infrastructure reliability, and governance. Collaborate with data scientists, analysts, and partners, enforce PHI security, mentor teammates, and support on-call and evolving BI technologies.

Design, optimize, and scale Snowflake-based data architecture and pipelines. Implement data observability, operationalize MLOps, build semantic enterprise models, and deploy AI-assisted agents and automation for production analytics and commercial data products. Share cross-functional ownership for pipeline resilience, monitoring, and model lifecycle management.