Top Hybrid Data & Analytics Jobs

Design, build, and scale reliable data infrastructure, including event ingestion, stream processing, data warehouses, ETL pipelines, analytics tools, and machine learning systems. Optimize performance, reliability, security, and data quality across AWS regions and data centers. Collaborate with analytics and data science teams, troubleshoot complex systems, influence architecture, conduct code reviews, and mentor data engineers while helping establish engineering best practices.

Lead design and implementation of enterprise Master Data Management (MDM) solutions, establish Golden Records, build cloud-native data pipelines and APIs, implement dbt transformations and IaC, enforce data quality, governance, and HIPAA-compliant practices to enable analytics, AI, and operational systems across Member, Provider, Facility, and related domains.

Build and own analytical data products, dbt models, semantic layers, shared metrics, and data quality systems. Model GA4 and digital analytics data, activate trusted data through reverse ETL, and partner with analysts, product teams, and business stakeholders to support decisions. Mentor engineers and analysts, establish modeling standards, and contribute to self-service and AI-assisted analytics foundations.

Leads customer data architecture for Salesforce Data 360 and Marketing Cloud programs. Defines ingestion, data models, identity resolution, household and vehicle relationships, audience eligibility, consent, data quality, activation, governance, and measurement foundations. Partners with client data, privacy, security, marketing, and Salesforce teams while coordinating third-party data providers and maintaining technical documentation.

Leads data strategy, analytics, visualization, reporting, governance, and knowledge-management initiatives supporting Army medical systems acquisition and program execution. Integrates and analyzes complex datasets, develops dashboards and predictive models, establishes data-quality practices, and communicates actionable recommendations to government leaders and cross-functional stakeholders. Supports data and document migration, executive briefings, logistics analysis, forecasting, and decision support.

Leads modernization of Lazard Asset Management’s data estate by defining enterprise data strategy, migrating legacy systems to a cloud-native platform, establishing DataOps, and delivering governed data products and APIs. The role owns architecture, security, regulatory controls, operational KPIs, AI-ready data, and production-safe migrations while building and mentoring a high-performing data engineering team.

Owns enterprise data quality, governance, metadata, lineage, and master data management across operational and analytical domains. Ensures data meets business, policy, regulatory, and contractual requirements; drives consistent metric definitions; oversees data issue resolution, compliance reviews, and quality standards throughout the data supply chain. Partners with business stakeholders, IT, engineering, risk, and compliance teams, and provides training on appropriate data usage.

Designs and optimizes scalable ELT/ETL pipelines using dbt, SQL, Snowflake, and Azure. Leads data architecture, dimensional modeling, performance tuning, CI/CD, data quality, observability, lineage, governance, and security. Partners with stakeholders to create reliable analytics assets, evaluates GenAI applications, mentors junior engineers, and supports modern cloud data operations.

Designs and leads enterprise database architectures, data models, integrations, governance, and performance initiatives. The role supports scalable and secure SQL Server environments, cloud platforms, data warehouses, ETL processes, reporting, analytics, and transportation safety data systems. Responsibilities include optimization, security, backup and recovery, documentation, technical standards, and collaboration with software engineering and business teams. The position requires strong relational database, SQL development, administration, and enterprise data modeling expertise.

Leads data warehousing process development, methodology, documentation, and standard operating procedures. Analyzes and documents program requirements, including mission objectives, resources, intelligence, operational needs, and stakeholder priorities. Evaluates workflow and production processes, providing assessments and recommendations to improve efficiency and strategy while guiding junior staff.