Top Data & Analytics Jobs

The Junior Data Engineer supports system implementations by designing metadata harvesting pipelines using SQL and Python, collaborating with SMEs to ensure data accuracy and documentation.

Design, build, and maintain data pipelines and backend systems to automate reporting and support cross-functional teams. Own project prioritization, collaborate with stakeholders (Accounting, Finance, Supply Chain, Publishing), and iterate to improve cost, reliability, and access to data across the organization.

Design, build, and maintain automated People data ingestion pipelines and analytics‑ready models. Support Databricks and Microsoft Fabric platforms, create certified Power BI datasets, enforce governance, monitor pipeline health, and enable trusted HR reporting and Workday readiness.

Build and maintain data pipelines and transformation layer using dbt and SQL; monitor replication and orchestration pipelines, add data quality checks, and develop datasets and dashboards for internal and client analytics. Collaborate with senior engineers, participate in code reviews, and gradually take on more ownership.

Design, build, and own the pursuit intelligence platform integrating GovWin IQ, SAM.gov, Salesforce, and D&B. Lead API integrations, ETL/ELT pipelines, account scoring/tiering engines, and data architecture. Provide technical leadership through code reviews, coaching, documentation, and standards to support business intelligence and Go/No-Go decisioning for Pursuit & Capture.

As a Senior Data Engineer, you'll design and build data pipelines and architectures, ensuring data quality and governance while mentoring peers.

As a Senior Data Engineer, you'll design and build data solutions, optimize data pipelines, and ensure data governance while mentoring team members.

The Senior Data Engineer will design and build data pipelines, manage data architecture, ensure privacy compliance, mentor team members, and collaborate with cross-functional teams.

The Senior Data Engineer will develop scalable data pipelines, drive automation, enforce standards in Big Data, and collaborate with stakeholders on data solutions.

The role involves building and improving WHOOP's data platform infrastructure, ensuring reliability and efficiency, and driving best practices across engineering and data workflows.

As a Senior Data Engineer, you will automate processes, optimize data handling, build data products, and govern data while mentoring junior engineers.

The Portfolio Chief Engineer will lead design and implementation of Data and Analytics Systems, working with customers and engineering teams to generate innovative solutions, engage in business development, and ensure technical excellence in large-scale information management.

Design, build, and maintain scalable data pipelines and transformations using Snowflake, Airflow, and CI/CD. Ensure data quality, de-identification, monitoring, and optimization. Collaborate with architects, analysts, and stewards to meet business requirements and implement robust, production-ready data solutions.

The Palantir Data Engineer develops data transformation pipelines, integrates external systems via API, and conducts code reviews in Palantir's environment. Responsibilities include managing data ingestion, developing ETL code, and collaborating with Data Scientists.

The Data Integration Lead designs and manages data ingestion pipelines, ensuring data integrity and security while supporting ETL processes in classified environments.

Build and maintain scalable data, model training, and inference pipelines for large multimodal 3D/4D microscopy datasets; implement multi-node GPU training/inference, ensure data provenance and quality, collaborate with scientists, document workflows, and support model deployment and public data release.

Design, build, and optimize data pipelines for massive datasets. Collaborate with stakeholders, implement CI/CD processes, and ensure data architecture scalability.

The Data Engineer II will manage analytics pipelines for mobile games, transforming telemetry data and ensuring data quality for studio teams' decisions.

The Principal Data Engineer will manage the analytics data architecture, engage cross-functionally, and create reliable data systems using Snowflake and Python on AWS.

Build and maintain data ingestion and transformation pipelines on Microsoft Fabric/Azure, prepare analytics-ready datasets, write and optimize SQL, validate data quality, create Power BI reports, collaborate with stakeholders, support deployments, testing, documentation, and ongoing dashboard enhancements for Sales and Marketing.

Design, build, and maintain scalable analytics solutions on Microsoft Fabric and Azure. Gather requirements, model data, develop ETL pipelines with SQL and Python, support data lake and governance, provide technical leadership, and create reports using Power BI/Tableau.

Design, build, and maintain ETL/data pipelines and data warehouse solutions. Ensure data quality, perform data modeling, support analytics and data science, act as SQL Server DBA, integrate third-party systems/APIs, optimize platform performance, collaborate with stakeholders and vendors, and manage platform roadmap and reporting backlog while following IT policies and Agile/CI/CD practices.

As a Data Engineer, you will design and maintain data pipelines, ensure data quality, integrate systems, and support reporting tools for decision-making.

Design, build, and maintain scalable data lakes, warehouses, and production ETL/ELT pipelines. Automate data pipelines, ensure data quality via testing, collaborate with architects, data scientists, and DevOps, integrate data solutions into client environments, and implement data recovery and security measures.

Design, build, and maintain scalable batch and near-real-time data ingestion, transformation, and curation pipelines using Databricks and AWS. Support full pipeline lifecycle (intake, profiling, development, testing, release), implement data quality and observability, enforce metadata/lineage/governance, integrate with enterprise services, and prepare curated datasets for SQL and BI consumption.