Top Data & Analytics Jobs

Embedded data engineer on the systematic China equity team responsible for end-to-end data lifecycle: sourcing, ingestion, modeling, validation, transformation, and delivery for research and production. Build scalable data pipelines, schemas, quality checks, and monitoring; profile datasets; collaborate with researchers, developers, and vendors; and improve tooling, standards, and vendor relationships to support alpha research and trading.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

Design, build, and operate scalable data pipelines and platforms for enterprise clients. Develop ETL/ELT workflows, data ingestion and processing for structured and unstructured data, guide cross-functional teams, and support monitoring, scaling, and operationalization of data solutions.

Design, build, and maintain scalable data pipelines and platforms. Implement ETL processes, collaborate with cross-functional teams, support analytics and ML workloads, and ensure data accessibility for mission-driven projects. Work occurs in agile teams and may involve cloud and on-prem infrastructure.

The Data Engineer, Senior will design and maintain data systems, build ETL pipelines, and collaborate with a team to optimize data solutions for client missions.

The Data Engineer will design and implement ETL frameworks, perform data analysis, troubleshoot data issues, and optimize data models to meet business needs.

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.

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.

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.

The Snowflake Data Engineer will work on data cleansing and harmonization, moving from legacy systems to SAP S4, focusing on ETL/ELT development and view creation.

This role involves designing, building, and maintaining data pipelines on Snowflake. Responsibilities include ensuring data quality and collaborating with stakeholders to deliver solutions.

The AWS Data Engineer will work on a data cleansing and harmonization project, focusing on AWS ETL design and development using various AWS services.

The Snowflake Data Engineer will design, build, and maintain data pipelines on Snowflake, optimizing performance and ensuring data quality while collaborating with stakeholders.

The Snowflake Data Engineer and Product Owner will manage the Snowflake data analytics project, collaborating with stakeholders, leading Agile practices, and ensuring quality deliverables through effective team coordination and backlog management.

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.