Top Data & Analytics Jobs

Design and implement end-to-end data integration and warehousing solutions for large volumes. Implement Hadoop/Hive data movement, script in Linux shell and Python, develop SQL for integration and analysis, and perform high-volume DW performance tuning. Vertica experience is a plus. Requires technical independence and a BS in CS/EE.

Design, architect and manage AWS Big Data solutions; build and tune Hadoop-based ETL/ELT pipelines; process unstructured and real-time data; develop with Java/Python/C++/scripting languages; collaborate on BI/visualization; ensure performance and robust workflows. 6-month contract, US work authorization required.

Lead design, development, and deployment of data reporting and dashboard solutions for Client Service Training. Work with project leadership to create tables/files, run ad-hoc reports, validate results, automate scheduled reports or integrate with dashboard tools, and communicate insights using BI and statistical tools.

Design, implement, and optimize Snowflake data storage and ETL solutions. Write and tune SQL, integrate data sources and BI tools, ensure data security and compliance, and collaborate with teams to meet business objectives.

Build and implement a transactional data system: design and develop SSIS ETL packages, consume web services in ETL, create SSRS reports for ETL status, and implement T-SQL/SQL Server solutions per functional requirements and system design.

Design, build and maintain a data transactional system performing ETL and web service calls to synchronize state between systems. Develop SSIS packages, data models, dictionaries, test plans, and assist architects on database strategy.

Develop ETL, reporting, and OLAP solutions using SQL Server (SSIS, SSRS, SSAS) and T-SQL; perform impact analysis, document requirements, support ETL/data quality, and assist database architecture and defect resolution.

Implement OLTP and OLAP systems to persist court information from functional requirements and system design. Perform ETL, export data to external agencies, use managed service calls, design and model database structures, design/code/deploy SSIS packages, document data dictionaries and package definitions, develop SSIS test plans, and support architects.

Design, develop, and optimize Teradata data solutions: elicit requirements, create logical/physical designs, write SQL (views/procs/macros), use Teradata utilities for data loads, tune performance for large datasets, coordinate stakeholders, and write Unix shell scripts.

Elicit and analyze requirements; design logical/physical Teradata solutions; develop ETL using Teradata utilities; write SQL (views, stored procedures, macros, functions); perform performance tuning for large datasets; write Unix shell scripts; coordinate with stakeholders and support BI/DW initiatives.

Build and own customer data ingestion and large-scale pipelines powering AI workers. Design syncing, retrieval, and data lake systems, improve reliability/observability, partner with product/backend teams, and contribute across infrastructure to give AI agents access to enriched customer data.

As an Analytics Engineer, you'll manage the data stack, design database architecture, build data pipelines, and oversee reporting and data quality.

Design, build, and maintain scalable data ingestion and ETL pipelines across a growing data lake. Define and enforce data quality standards, SLOs, and validation frameworks. Optimize pipelines for performance and cost, expand monitoring and alerting, and enable stakeholders to self-serve on data infrastructure.

Build and maintain Azure Data Factory ingestion pipelines into Snowflake, implement medallion transformations (Coalesce), write Python orchestration and connectors, apply governance with Atlan, collaborate with AI coding agents, participate in CI/CD with Azure DevOps, handle on-call production support, and document runbooks.

Lead and mentor a data engineering team, define technical vision and strategy, oversee data pipeline design, and collaborate with cross-functional teams.

Lead design and delivery of complex data solutions including data integration, cleaning, transformation, lineage, and migration. Build and document data pipelines, manage ADS and regulatory data lineage, lead testing and cutover for database migrations, create automation and reconciliation scripts, troubleshoot with architects and security teams, and mentor other data engineers.

The Data Engineer will execute data architecture projects, manage data ingestion processes, ensure data quality, and transition data to new systems while collaborating with investment teams.

Design, build, and maintain enterprise analytics solutions and semantic data models using Microsoft Fabric, Databricks, SQL, Python, and BI tools. Deliver scalable reporting datasets, executive dashboards, data quality standards, and CI/CD practices. Lead architecture discussions, mentor engineers, collaborate with stakeholders, and drive analytics strategy to enable data-driven decisions.

Design, build, and maintain enterprise analytics solutions and semantic data models using Microsoft Fabric, Databricks, SQL, Python, and Power BI. Develop reporting datasets, dashboards, and self-service analytics; ensure data quality, performance, CI/CD, and governance; translate business requirements into scalable solutions; provide technical leadership, mentor engineers, and collaborate with architects and stakeholders to align analytics with business objectives.

Design, build, and maintain enterprise analytics solutions, semantic models, and reporting datasets using Microsoft Fabric, Databricks, SQL, Python, and BI tools. Collaborate with product, platform, and business teams to ensure data quality, performance, scalability, and metric governance. Mentor engineers, lead data design discussions, and drive analytics engineering best practices to enable self-service analytics and executive reporting.

Design, build, and maintain enterprise analytics solutions, semantic models, and reporting datasets using Microsoft Fabric, Databricks, SQL, Python, and BI tools. Lead data modeling, data quality, CI/CD, and performance optimization; translate business requirements into scalable analytics; mentor engineers and collaborate with architects and stakeholders to deliver trusted, self-service and executive reporting across the Americas.

Design and implement large-scale GCP-based data platforms and ETL using PySpark/Spark, BigQuery, Dataproc, Composer/Airflow. Process and analyze large datasets, write SQL, collaborate on BI delivery, and work in a mature Agile team.

Build and maintain scalable cloud data pipelines and governance to support analytics across claims, EHR, and real-time patient data. Deploy and operate Snowflake/dbt/AWS-based infrastructure, enable real-time analytics, partner with clinical, product, and engineering teams, and deliver insights that inform care delivery, payer collaboration, and business decisions.

Build and maintain data pipelines and models to provide high-quality data for reporting, BI, and analytics. Work with SQL, dbt, Python, cloud data stores, orchestration tools, and reconciliation systems to support analytics, BI tools, and data warehouse delivery.

Design, build, and maintain ETL/data pipelines and data models (dbt). Work with large datasets across RDBMS and NoSQL, deploy to cloud (AWS preferred), use Python and orchestration tools (Dagster/Prefect/Airflow), support BI/analytics and visualization, and implement data reconciliation for accuracy.