Top Data & Analytics Jobs

Support building and testing digital solutions for production lines: analyze operational workload, assist with big data analytics and prototypes, troubleshoot production/maintenance tasks, and communicate insights under mentorship.

As a Senior Lead Data Engineer, you'll lead development teams, design technical solutions, utilize big data and cloud technologies, and drive transformation initiatives.

The Lead Data Engineer will design and build data architectures, lead a team, and develop ETL processes using cloud technologies to optimize data solutions for the U.S. business.

Lead design and build scalable, low-latency, fault-tolerant data platforms and pipelines. Mentor engineers, influence data architecture and governance, build analytics stores using big-data and cloud technologies, collaborate with product and security teams, and ensure CI/CD, resiliency, testing, and performance for data-driven solutions.

Design and maintain scalable data pipelines, models, curated datasets, analytics solutions, and manufacturing automation tools. Support Ignition and Sepasoft MES systems, integrate manufacturing software, and develop solutions for metrology and image inspection data. Apply software engineering practices and independently lead complex cross-functional projects from requirements through deployment.

Owns analytics datasets and data products for Netflix’s cloud games infrastructure. Builds scalable pipelines, data models, metrics, dashboards, tooling, and self-service assets from massive telemetry data. Partners with infrastructure engineering, data engineering, and data science teams to improve quality of experience, capacity, and utilization insights. Responsibilities include telemetry and logging, visualization, trustworthy documentation, scalable data solutions, and independently prioritizing high-impact investments.

Design, develop, and maintain scalable data pipelines and models in Databricks and Microsoft Azure. Perform data ingestion, transformation, integration, migration, and modernization across enterprise data sources. Monitor and optimize pipeline performance, reliability, and data quality. Translate business requirements into technical solutions, collaborate with architects and engineers, follow security and governance standards, participate in code reviews, and document data platforms and processes.

Design, build, and maintain scalable data stores, ETL/ELT pipelines, and data-processing software for structured and unstructured data. Collaborate with cross-functional teams to deploy, monitor, and operate data platforms and support analytics and machine learning workloads. Support assessment, design, and maintenance of scalable data solutions for mission-driven clients. Must be able to obtain Secret clearance and hold a bachelor’s degree.

Support building and maintaining ETL pipelines using Python and PySpark. Work with AWS services (Glue, Lambda, Step Functions, S3, Redshift, RDS, EMR) to ingest, transform, and validate data. Write complex SQL, implement basic monitoring/logging, process API/JSON payloads, and collaborate with engineers, analysts, and stakeholders on deployment and documentation.

Assist in building and maintaining ETL pipelines using Python and PySpark; develop workflows with AWS Glue, Lambda, and Step Functions; work with S3, Redshift, RDS, and Oracle; write complex SQL for extraction, transformation, and reporting; implement monitoring, logging, and error handling; ingest API/JSON data; collaborate with engineers, analysts, and stakeholders; support code management, documentation, and deployments.

Build and maintain scalable data pipelines, analytics data warehouses, ETL processes, data models, and infrastructure supporting reporting, analytics, and machine learning. Ensure data quality from creation through presentation layers, translate stakeholder needs into deliverables, improve tooling and pipeline reliability, and participate in on-call support. The role involves modern big data architecture, containerization, cloud platforms, and data engineering technologies.

Designs and scales streaming and batch data pipelines supporting AI applications and advertising technology. Builds embedding workflows, vector-store integrations, retrieval systems, AI-ready data models, and reusable ingestion components. Ensures observability, data quality, schema governance, reliability, recoverability, and auditability. Partners with AI, infrastructure, product, and governance teams to deliver production-ready systems, leads technical execution, mentors junior engineers, and influences architecture.

Design, build, and maintain data structures, ETL/ELT pipelines, and data products. Support operational data processes, performance tuning, data governance, documentation, and stakeholder collaboration. Mentor junior staff and improve data platform quality and accessibility.

Own and architect the company's semantic and metric layer: build certified dbt datasets, define canonical metrics, ensure data quality, partner on pipeline architecture, set modeling/testing/CI standards, and mentor analysts to scale analytics across factories.

Design and implement scalable data pipelines and orchestration on GCP to support AI/ML-driven mail intelligence. Build stream and batch processing, ensure data governance and quality, optimize workflows, create visualizations, and collaborate with US cross-functional teams to align technical strategy and recover from failures.

Design, build, and maintain end-to-end data ingestion, processing, storage, and access solutions. Implement ETL/ELT pipelines, data modeling, data lakes/warehouses, APIs, monitoring, and documentation. Collaborate with data scientists, analysts, and stakeholders to deliver scalable, secure, production-ready data services.

Design, develop, maintain, and optimize ADABAS database structures and data solutions. Support mainframe data processing (Natural, COBOL, JCL), perform migrations to modern platforms, tune performance, ensure data integrity/security, troubleshoot issues, and collaborate with developers, architects, and analysts on modernization, API/cloud integration, and data warehousing initiatives.

Build and enhance Databricks-based platform capabilities and reusable data products for quantitative researchers and analysts. Develop and operate pipelines integrating heterogeneous data sources, while improving governance, automation, observability, security, and operational excellence. Define target-state platform architecture, evaluate emerging technologies, and use infrastructure as code, CI/CD, Apache Airflow, and AI-assisted development tools to accelerate prototypes into production.

Design, build, and maintain data pipelines and platforms using Palantir Foundry; collaborate with analysts, engineers, and stakeholders; deploy scalable ingestion, transformation, and infrastructure automation; support agile teams and code reviews; work on mission-driven analytics requiring security eligibility.

Design, implement, and maintain scalable ETL pipelines to ingest and transform street and roadway data for transportation network analysis. Automate and optimize data workflows, collaborate with data providers and engineers, analyze routing errors, and document data models, workflows, and best practices.

Design, build, maintain, and optimize data infrastructure (databases, data lakes, warehouses) and ETL/ELT pipelines. Ensure data quality, security, performance, and compliance. Collaborate with analysts and stakeholders, document policies, and support monitoring, backup, and recovery strategies.

Build and maintain Tableau dashboards, certified semantic models, dimensional data models, and Snowflake data warehouses integrated with Aurora. Apply Python, SQL, version control, and CI/CD to analytics assets while ensuring data quality, governance, lineage, observability, performance, and scalability. Partner with stakeholders to translate reporting needs into trusted, self-service business intelligence.

Lead data engineering initiatives for pharmaceutical and biotech clients by building cloud-based data pipelines, warehouses, processing systems, analytics, and visualization tools. Responsibilities include analyzing business requirements, transforming big data, identifying insights, advising on standards and best practices, estimating engineering efforts, collaborating with stakeholders and leadership, and supporting growth of the data science business.

Design, build, and optimize large-scale ETL/ELT pipelines and lakehouse architectures on cloud platforms using Databricks, Spark, Delta Lake, and Snowflake. Lead data federation with Starburst, improve performance and governance, support AI/ML projects (RAG/Agentic AI), mentor engineers, manage stakeholders, and deliver solutions in an Agile environment.

Designs, builds, deploys, and operates scalable data pipelines and cloud-based data architectures supporting lending analytics across Mortgage and Personal Loans. Responsibilities include data ingestion and transformation, schema design, SQL optimization, workflow orchestration, monitoring, troubleshooting, data quality, and generative AI integration. The role also establishes engineering standards and collaborates with analysts, engineers, and governance teams to deliver reliable production data systems.