Top Data & Analytics Jobs

Leads the Health Fund’s Data Engineering Team and oversees enterprise data warehouses, pipelines, integrations, governance, testing, and data quality initiatives. Provides technical leadership, manages staff, translates business requirements into scalable solutions, and partners with business, technology, QA, infrastructure, and external vendors. Ensures data platforms meet performance, reliability, privacy, security, regulatory, and operational requirements while developing data strategy, architecture, documentation, standards, and best practices.

Designs and implements data architecture solutions, including data models, data flows, dictionaries, warehouses, and data lakes. Integrates data from multiple sources, ensures quality, integrity, and security, troubleshoots architecture and ETL issues, reviews technical configurations, and guides development teams. Collaborates with stakeholders, stays current on data management practices, and mentors junior staff supporting government programs.

Deliver analytics consulting projects involving process automation, data visualization, statistical analysis, and analytical tool development. Partner with stakeholders to identify business value, build minimum viable products and production applications, and train business users. Use tools such as Dataiku, Alteryx, Tableau, Power BI, Sigma, SQL, Python, cloud platforms, and modern databases while managing projects independently.

Build and maintain enterprise data pipelines and integration workflows across on-premises and cloud systems. Ingest, transform, and stage structured and semi-structured data; write and optimize SQL queries, views, and stored procedures; implement data validation, monitoring, alerts, and logging; document pipeline logic; use source control and code reviews; and provide support through tickets and on-call rotations.

Develop and maintain manufacturing data infrastructure, databases, dashboards, and analytical tools integrating fab, equipment, metrology, test, yield, and reliability data. Build Python and JMP workflows, automate reporting and alerts, ensure data traceability and quality, troubleshoot dashboard and data-connection issues, and support SPC, DOE, process capability, reliability, and root-cause analyses. Collaborate with engineering, manufacturing, quality, reliability, and IT teams.

Leads the customer master data governance program and serves as business owner for the Informatica MDM platform. Oversees governance policies, data quality, customer hierarchies, stewardship operations, source onboarding, platform enhancements, and AI-enabled matching and deduplication. Partners with business, IT, technical teams, and external providers to define requirements, manage priorities, resolve issues, and improve data processes. Performs hands-on testing, validation, reconciliation, and user acceptance activities before platform changes are deployed.

Lead a small analytics team (staff data scientist + two analysts) to deliver curriculum, product, and program insights. Split time between people management (60-70%) and hands-on analysis (30-40%), advance experimentation and measurement, build AI/LLM-driven agentic workflows, manage sprints and cross-functional stakeholder relationships, and drive analytics that inform curriculum, product, and intervention strategies.

Build and maintain a governed analytics platform in Snowflake using dbt, including dimensional models, data transformations, tests, contracts, semantic layers, and migrated reporting. Integrate diverse operational sources, ensure data quality, document models, and collaborate through GitHub pull requests. Use AI coding tools responsibly while validating correctness, performance, and maintainability.

Design and build PerfectServe’s unified cloud data platform, including ingestion pipelines, integrations, data models, metrics, and consumer-facing data layers. Establish architecture, engineering, quality, and testing standards; enable safe extraction from production systems; partner with data users; and mentor data engineers. The role requires substantial cloud data platform experience, deep SQL, warehouse expertise, ELT, change-data-capture, AWS, modeling, orchestration, and data quality skills.

Build and own marketing data, attribution, tracking, audience sync, and conversion infrastructure. Integrate product, CRM, warehouse, and advertising platforms; send offline conversions and LTV signals to ad platforms; maintain event schemas and dashboards; debug data discrepancies; implement privacy-safe tracking; and partner cross-functionally with growth, product, marketing, and data teams.