Top Hybrid Data & Analytics Jobs

Leads Healogics’ data engineering team and enterprise data platform strategy. Owns SQL Server warehouse reliability, ETL, OLAP, SSRS, data architecture, modeling, governance, security, and production support while directing migration to Databricks. Translates business needs into scalable data solutions, establishes engineering standards, manages vendors and implementation partners, and partners with executives, analytics, application, cybersecurity, and infrastructure teams.

Build and maintain data pipelines, transformations, integrations, and workflow orchestration using Snowflake, SQL, Python, Airflow, and related tools. Support data quality, governance, metadata, documentation, troubleshooting, production operations, and API integrations. Collaborate with product, engineering, analytics, and business teams in an Agile environment while developing reliable, reusable data solutions.

Leads the enterprise data platform engineering team and owns its reliability, security, scalability, performance, cost, and operational support. Oversees relational, NoSQL, and analytical platforms; data movement and transformation; infrastructure as code; GitOps; database automation; high availability and disaster recovery; monitoring; compliance; and incident response. Manages engineers, on-call operations, technology selection, vendor evaluations, budgeting, and cross-functional delivery while remaining hands-on in architecture, administration, performance tuning, and complex technical escalations.

Lead global strategic intelligence activities, including market and competitive analysis, industry and geographic assessments, market-share reporting, executive presentations, third-party analyst submissions, and annual market updates. Partner with business units, executives, sales, marketing, finance, and external organizations to develop actionable insights. Manage strategic intelligence projects, improve reporting processes, maintain the market intelligence portal, and support AI and data visualization capabilities.

Build and maintain production-grade data pipelines, connectors, and harmonized snapshots across clinical studies and applications. The role covers ingestion, transformation, orchestration, data modeling, quality controls, monitoring, lineage, auditability, regulatory compliance, CI/CD, and integration with Databricks and Snowflake environments. The engineer will validate source data, manage schema changes, optimize performance, and collaborate with platform and AI engineers to support reliable data and AI applications.

Leads Oscar Health’s Analytics Engineering function, setting enterprise data strategy, operating models, governance, quality, lineage, and observability standards. Oversees multi-team organizations, builds trusted reusable data products, expands self-service analytics, enables responsible AI, and partners with executives on transformation priorities, investments, and outcomes. Develops managers and senior technical talent while improving data reliability, delivery speed, adoption, and cost efficiency.

Analyzes complex insurance data and business processes to develop reporting, data models, proprietary tools, and analytical solutions. Partners with internal teams, agents, and customers to gather requirements, communicate insights, improve processes, and support business growth. The role includes project management, report design, database modeling, quality review, stakeholder presentations, and mentoring less experienced team members.

Leads discovery and requirements gathering for enterprise data solutions, translating business needs into user stories, acceptance criteria, source-to-target specifications, and data workflows. Partners with engineering, architecture, governance, analytics, and business teams throughout delivery. Coordinates validation, reconciliation, testing, defect resolution, and stakeholder approvals while managing risks and dependencies. Documents data lineage, integrations, quality expectations, and reporting needs, and mentors analysts and cross-functional partners.

Build and maintain dbt models and data pipelines feeding Snowflake from agency, CRM, HR, finance, and other sources. Onboard data sources, improve testing and observability, troubleshoot nightly runs, optimize Snowflake performance and costs, and contribute to governance controls. Develop backend data layers for internal applications, support CI pipelines, and collaborate with analysts and business stakeholders to deliver reliable, documented data models.

Investigate difficult public and municipal data sources, reconstruct and validate datasets, design data models, and build production systems for acquiring and refreshing data. The role partners with operations and state expansion teams, prototypes solutions, reviews TypeScript, uses PostgreSQL and spatial data, and owns reliable production implementations supporting affordable housing site selection.