Top Data & Analytics Jobs

Build and lead the data engineering ecosystem supporting analytics, BI, and data science. Design scalable pipelines, cloud infrastructure, Snowflake data warehouses, governance controls, validation frameworks, and machine learning workflows. Collaborate with engineering, data science, product, and business stakeholders on architecture and technology decisions. Develop AI agents and secure enterprise AI integrations, participate in on-call and incident management, document standards, and mentor data engineers.

Designs and builds scalable batch and real-time data pipelines, ingestion workflows, analytical processes, data products, and intelligent APIs. Ensures data quality, reliability, and performance while supporting Azure-based architecture for BI and AI/ML use cases. Collaborates with cross-functional teams, participates in code reviews, and follows DevOps, Agile, and CI/CD practices.

Designs and maintains batch and streaming data pipelines using Databricks, AWS, Python or Scala, SQL, and orchestration tools. The role covers data quality, governance, cataloging, lineage, API integration, and regulated pharma data environments. Responsibilities include translating business needs into technical specifications, partnering across pharma functions, presenting to executives, prioritizing data initiatives, supporting adoption, and building new data domains. The engineer also applies warehousing, dimensional modeling, documentation, and stakeholder coordination practices.

Designs and maintains enterprise ETL and data integration pipelines, develops scalable data warehouse solutions, optimizes SQL and data workflows, and supports migration of legacy platforms to Google Cloud. The role focuses on data quality, performance, reliability, troubleshooting, and collaboration with technical and business stakeholders.

Build and maintain scalable data pipelines and architectures for AI and machine learning use cases. Prepare structured and unstructured datasets for training, fine-tuning, and inference while ensuring data quality, reliability, and governance. Collaborate with ML and AI teams to support data requirements and solve complex system-design challenges using technologies such as Airflow, dbt, Spark, data warehouses, and streaming platforms.

The Principal Data Engineer will lead the design and development of scalable data platforms, optimize data pipelines, and collaborate with multiple teams to enhance data solutions.

Own end-to-end data work: build and maintain dbt models and pipelines, ensure warehouse data quality, design Tableau dashboards, automate recurring reports, partner with stakeholders to translate needs into analytical products, and leverage Python and AI tools to improve data infrastructure and analytics.

The Data Engineer II designs, develops, and maintains data integration and analytics solutions, ensuring data quality and accessibility for business stakeholders, while collaborating with teams to meet business needs.

The Data Engineer will design, develop, and implement data solutions, ensuring data integrity and availability, collaborating with stakeholders to meet business analytics needs.

Design, build, test, and maintain data architectures, database systems, and pipelines using GCP and BigQuery. Optimize data retrieval, automate processes, redesign infrastructure for scalability, collaborate with stakeholders, and deliver analytics tools that provide actionable business insights.

The Data Engineer is responsible for building and maintaining data pipelines, ensuring data quality and reliability, and collaborating with various teams on data needs. This involves working with AWS tools and programming in Python and SQL to support analytics and infrastructure.

Build and maintain scalable batch and streaming data pipelines for large and semi/unstructured data to support reporting, data science, ML training/execution and search analytics. Evaluate and deploy data technologies, collaborate with architects, data scientists and business partners, and handle coding, testing, deployment, monitoring, documentation and automation in an Agile environment.

Design, build, and maintain scalable data pipelines, client integrations, and data models to support reporting, analytics, and AI/ML. Ensure data quality, monitoring, and reliable ingestion of healthcare (FHIR/HL7, EHR) and claims data. Collaborate with product, engineering, and AI teams to enable downstream products and analytics.

Design, build, and optimize end-to-end data pipelines and dataflows, integrate with AWS, maintain schemas/data dictionaries, provide Tier 3 support, collaborate with stakeholders and product teams, document architectures, and support off-hours deployments. Ensure data governance, scalability, and production readiness while working primarily on-site in a cleared defense environment.

The Data Engineer will develop tools and code for data engineering activities, support data transport and ingestion, and work alongside Data Scientists. The role requires hands-on coding and ETL responsibilities, ensuring data is correctly formatted for analysis.

Build and maintain scalable GCP-based data pipelines, ETL/ELT processes, and data models. Support migrations from legacy systems, enable ML integrations, enforce data quality and lineage, document Terraform modules, implement CI/CD across environments, and provide production support and performance tuning within Agile teams.

The role involves developing and maintaining the Enterprise Data Warehouse, optimizing queries, and supporting data analysis and business intelligence activities.

Design, build and support EDW/data mart and Hadoop-based data ingestion and integration solutions. Support development, SIT and UAT investigations, coordinate with users, and deliver reliable data pipelines.

Designs and implements cloud-first, event-driven data solutions and ETL pipelines for data warehouses (Snowflake/Oracle). Leads architecture, CI/CD, QA, and Agile delivery; mentors teams; builds scalable backend systems using AWS, APIs, and automation tools. Ensures project delivery on time and within budget while advising senior management on technical strategy.

Design, build, and maintain scalable data pipelines and platforms for banking data. Collaborate with teams to ensure secure, reliable data products.

As a Data Engineer at OpenAI, you'll design and implement data pipelines, develop key datasets, and collaborate with teams to integrate data for People Innovation Labs' products.

Build and maintain ETL/ELT data pipelines and storage for batch and real-time use. Partner with product and engineering to define requirements, ensure data quality, monitor pipeline health, integrate cloud data warehouses/lakes, optimize SQL/Python transformations, and document data flows and architecture.

The Data Engineer will develop and implement data pipelines, manage ETL processes, and work with big data in a collaborative team environment.

Design, build, and operate data pipelines for structured and unstructured sources, performing ingestion, cleaning, transformation, quality checks, lineage capture, and monitoring. Ensure compliance with DoD governance and security, troubleshoot pipeline failures, maintain runbooks, and collaborate with stakeholders to deliver scalable, governed data products on Army enterprise platforms using Vantage/Foundry tools.

Lead design and delivery of Microsoft Fabric and Azure-based data lakehouse and warehouse solutions. Own data ingestion, ETL, modeling, governance, security, CI/CD, and Power BI integration. Architect GenAI/RAG solutions, mentor a small engineering team, define standards and playbooks, and partner with business and tech stakeholders to execute multiple concurrent implementations.