Top Data & Analytics Jobs

Develop, optimize, and support ETL/data integration solutions (SSIS) to automate data delivery. Collaborate with analysts, senior developers, and architects; monitor executions, troubleshoot data issues, and follow established SDLC practices. Hybrid work with periodic on-site presence.

Design and maintain production data pipelines and architecture across Snowflake and Databricks, scale Terraform deployments, implement data governance, build demo data processes, and collaborate cross-functionally to support analytics and product use cases.

Design, build, and operate production data pipelines and architectures across Snowflake and Databricks. Scale demo data across cloud warehouses, partner with infrastructure for high availability, implement data governance, and collaborate cross-functionally to support analytics and GTM use cases.

As a Data Engineer, you will optimize data pipelines, develop code in Python and Spark, manage cloud infrastructure, and drive data solutions.

As a Data Engineer, you will manage data pipelines, automate processes, and collaborate on software solutions to optimize supply chain performance.

Embed with the Asia Commodities desk to own vendor data feeds, build scalable data pipelines and dashboards, transform market and supply/demand data into analytical constructs, support research-to-production workflows, and collaborate with investors and platform teams to enable systematic commodities strategies and trading decisions.

Design, build, and maintain scalable batch and streaming data pipelines. Write clean code (Python, SQL, Spark), ensure data quality and reliability, monitor and troubleshoot pipelines, document sources and transformations, and collaborate with analysts, data scientists, and product teams to deliver data products and support analytics and ML use cases.

Design, develop, and maintain scalable data platforms, ETL/ELT pipelines, and tabular/dimensional data models across on-premises and cloud environments. Build and schedule data processing jobs, ensure data quality, performance tuning, and troubleshooting. Lead code reviews, participate in on-call production support, mentor engineers, and drive best practices and technical debt remediation.

Design, build, and maintain enterprise data warehouse and data lake solutions. Develop, tune, and operate ETL pipelines and SQL for analytics. Own end-to-end features, collaborate with analytics and engineering teams, and evolve data storage and ingestion platforms.

Serve as chief architect for a unified, Snowflake-managed Apache Iceberg Data Lakehouse on AWS. Design scalable ingestion (batch/stream), multi-tenant data mesh and RBAC, semantic layer for BI/AI, standardize medallion ELT (dbt, PySpark, Snowpark), automate with Terraform, mentor senior engineers, and drive cross-org integration and stability.

Lead design, build, and maintain enterprise-scale data platforms and pipelines. Mentor cross-functional teams, develop software to process structured/unstructured data, and operationalize ETL/ELT workflows for analytics in agile environments supporting mission-driven projects.

Design, build, and maintain scalable data pipelines, lakehouse/data‑warehouse architectures, and ETL/ELT workflows to support analytics and AI/ML. Ensure data quality, lineage, monitoring, and SLA-driven reliability; support project delivery, client communication, and business development under project management guidance.

Design, build, and operate cloud-based data engineering solutions on GCP. Develop scalable batch and streaming pipelines, perform data modeling and transformations, optimize ETL/ELT workloads, monitor pipelines, and collaborate with stakeholders and BI teams to deliver enterprise data solutions in a Scrum environment.

Design, build, and own a scalable, HIPAA-compliant data management system that fetches partner data (e.g., EPIC), extracts semantic knowledge, summarizes provider notes, and provides user-facing UI for viewing and updating data to power provider matching, scheduling, and recommendation products.

Design and build data systems and analytics to turn large, structured and unstructured datasets into actionable insights. Develop and validate AI/ML models (including LLMs/agentic AI), clean and retrieve data, visualize results for non-technical leaders, and work on classified networks supporting DoW/IC clients.

Lead data engineering and advanced analytics work for DoD/IC clients: design and build AI/ML models (including LLMs/agentic AI), clean and retrieve data, visualize results for non-technical leaders, guide teammates, and operate on classified networks with required TS/SCI clearance and polygraph readiness.

Design, build, and maintain scalable data pipelines and platforms. Convert and organize structured and unstructured data for analytics, implement ETL/ELT workflows, support data stores, collaborate with cross-functional teams, and operate data platforms in controlled/secure environments to support mission-driven projects.

Lead data engineering efforts to turn large, disparate datasets into actionable insights. Build and evaluate advanced analytics and AI/ML models (including LLMs/agentic AI), visualize results for non-technical leaders, work on classified networks, guide teammates, and advise clients. Requires conveying complex findings clearly and making go/no-go recommendations.

Build reusable, analytics-ready telematics data products on Azure Databricks. Design canonical datasets, standardize metrics and business logic, optimize performance and cost, enable self-service analytics, and support AI/ML use cases for enterprise telemetry and connected-vehicle data.

Build, scale, and operate an enterprise telemetry data platform that ingests, processes, and publishes vehicle telemetry at global scale. Develop batch and streaming pipelines using Databricks and Spark, implement observability and data quality controls, optimize distributed workloads for performance and cost, and enable reliable production data products through CI/CD and Infrastructure as Code. Provide technical leadership and partner with analytics, AI, and engineering teams.

Design and implement scalable data models and ETL pipelines (batch and streaming), ensure data quality/SSOT, collaborate with product and platform teams to resolve issues, contribute to data governance, and explore practical integrations between data engineering and AI.

Build and operate scalable data pipelines and services to power internal products. Design data models, curate high-quality datasets, collaborate with cross-functional teams, and mentor engineers while improving platform reliability, observability, and AI-ready data layers.

Lead Data Engineer will design, build, test, and support cloud-based data solutions and streaming systems. Collaborate with Agile teams and product managers, mentor engineers, implement big data tooling (Spark, Kafka, EMR), and optimize data warehousing (Redshift, Snowflake) using Java/Scala/Python and SQL. Ensure code quality with unit tests and reviews.

Lead Data Engineer designs, develops, tests, implements, and supports cloud-based data solutions. Collaborates with agile teams, mentors engineers, builds streaming and batch pipelines using big-data and cloud technologies, and ensures performant, well-tested code.

Lead Data Engineer designs, builds, tests, and supports cloud-based data platforms for marketing and messaging. Collaborates across Agile teams to deliver scalable, real-time data and ML-enabled solutions using distributed systems, data warehousing, ETL, and streaming technologies. Mentors engineers and participates in code reviews and performance tuning.