Top Data & Analytics Jobs

Design and build high-volume, low-latency data solutions: ingest and normalize diverse data sources, implement ETL and streaming pipelines, build CI/CD for data software, and maintain observability and monitoring.

Develop and maintain data pipelines and BI solutions for smart factory initiatives. Design data models, document data warehouses, build KPI dashboards, support ad-hoc analysis, and collaborate with stakeholders to enable data-driven decisions.

Build and maintain batch ETL/ELT pipelines and Airflow orchestration using Python, SQL, and AWS. Develop operational datasets for fulfillment, inventory, shipments, and returns. Monitor and optimize pipelines, implement data quality checks and alerting, support production incident response, and collaborate with engineering, BI, and operations teams to improve pipeline scalability, observability, and reliability.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

The Data Engineer will develop and implement ETL processes, design data architectures, work with data platforms, and ensure data solutions meet scalability and security needs. Responsibilities include mentoring junior engineers and collaborating with stakeholders to gather requirements and optimize data transformations.

Lead analytics engineering initiatives, design data pipelines, develop business intelligence solutions, and ensure data governance while collaborating with stakeholders.

The Data Engineer will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

The Data Engineer will architect and develop robust data frameworks for data platforms, focusing on data transformations and automated workflows. Responsibilities include collaborating with stakeholders, ensuring data quality, and maintaining high engineering standards.

Design and operate scalable data systems for People Analytics, ensuring reliability and privacy of sensitive employee data. Collaborate with leadership to create data products for decision-making.

Design, develop, and optimize scalable batch and real-time data pipelines and data warehouses using Spark and SQL. Deploy and manage containerized data workloads (Docker, Kubernetes/OCP), implement CI/CD, manage object storage and modern table formats (Apache Iceberg), optimize performance, and drive data architecture, governance, and platform modernization while mentoring engineers and collaborating with stakeholders.

The Data Engineer will help build modern data infrastructure, designing ETL pipelines, modeling datasets, and collaborating with teams for improved data quality and accessibility.

As a Data Engineer II, you will build, scale, and optimize data platforms, focusing on ETL/ELT pipelines, and collaborate with data scientists and AI engineers to support data-driven applications and insights.

The Data Engineer will design and build scalable data systems, contribute to the architecture of the data platform, and collaborate with AI/ML engineers to enhance automation and intelligence in Canoe's systems.

Develop, curate, and publish authoritative geospatial data products in ArcGIS Portal; build Python data-processing pipelines; and create Tableau dashboards and web maps. Manage metadata, hosted feature layers, and data discoverability while partnering with mission users to transform large spatial and tabular datasets into operational visualizations and automated workflows.

Design, build, and operate scalable data platforms and ETL/ELT pipelines using Python, SQL, Airflow, and AWS. Optimize data warehouses, ensure data quality and governance, implement CI/CD and infrastructure-as-code, and collaborate with cross-functional teams while mentoring engineers.

Design, build, and operate big-data and streaming platforms (Kafka, Hadoop, Dremio). Develop, deploy, and monitor data pipelines using Java, Python, Spark/Flink; collaborate on data modeling and ingestion, ensure data integrity and availability, act as a Big Data SME, and perform root-cause analysis.

Lead design, build, and operate scalable AWS data solutions (ingestion, batch/streaming, transformation, modeling, warehouses/lakes). Collaborate with product and analytics teams, enforce data governance, debug production issues, and drive best practices, automation, and operational excellence.

Design, build, and maintain Databricks-based ETL/ELT pipelines and medallion-layer data products. Integrate enterprise applications (including DataStage and ServiceNow), implement data quality, governance, and operational support, and collaborate with cross-functional teams to deliver scalable, production-ready data assets for analytics, reporting, and AI.

As a Data Engineer II at Magnite, you will build and optimize data pipelines with Apache Spark and Python, collaborate on data architecture, ensure operational performance, and work on infrastructure provisioning using Terraform and AWS.

As a Senior Associate in Data Engineering at PwC, you will design and implement data architecture strategies, analyze complex issues, mentor junior members, and build client relationships while ensuring adherence to project standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

As a Manager in Managed Services, you'll lead data ecosystems projects, mentor juniors, manage client accounts, and deliver data strategies to optimize operations and drive insights for clients.

Support design, build, and maintenance of scalable data solutions and infrastructure. Perform data requirement analysis, data preparation, migration and transformation. Collaborate to ensure availability, security, and maintainability of data services across multiple platforms.

Design, build, and maintain scalable data services and infrastructure across multiple data platforms. Work with users, developers, operations, and business partners to define requirements, implement data migration and transformation processes, and ensure data accuracy, security, availability, and maintainability.