Top Hybrid Data & Analytics Jobs

The Lead Data Engineer will architect and develop reporting solutions, integrate AI, analyze complex datasets, and mentor team members to enhance data-driven decision-making.

Leads enterprise data management strategy, governance, quality, reporting, workflow optimization, and operational risk initiatives in a regulated financial services environment. Partners with senior stakeholders to deliver cross-functional programs, dashboards, controls, analytics, and process improvements. Oversees AI and GenAI applications for reporting, documentation, exception management, and process mining while ensuring privacy, model risk, responsible AI, and regulatory compliance.

Build and maintain Power BI and Oracle OTBI workforce dashboards covering headcount, turnover, recruiting, and compensation. Analyze ad hoc HR data requests, support executive reporting, validate and govern HR data, investigate anomalies, document methodologies, and partner with HRIS and BI teams on data pipelines and reporting processes. Provide training and technical support to HR users.

Supports the Data Intelligence & Advisory team by gathering business requirements, documenting user stories, participating in Agile ceremonies, translating requirements into technical designs, exploring wireframes, supporting testing and troubleshooting, and assisting with tool deployment planning. The intern works under direct supervision while collaborating with product owners, business users, development, testing, vendors, and internal teams.

Own and evolve the end-to-end product analytics platform, including instrumentation, event collection, ingestion, raw data storage, data pipelines, analytical models, metrics, dashboards, quality monitoring, observability, lineage, and governance. Partner with software engineers to ensure analytics data is accurate, scalable, traceable, and reliable for business and product decision-making.

Develop and maintain enterprise data architecture, data and API strategies, standards, integration approaches, governance frameworks, and roadmaps for the Judiciary. Assess data sources, storage, APIs, security, privacy, compliance, analytics, and interoperability. Guide data warehouse and data lake solutions, data quality metrics, migration from on-premises to cloud, dashboard development, emerging technology evaluations, and stakeholder collaboration across technical and nontechnical teams.

Own and mature the enterprise data governance program, including policies, critical data elements, quality frameworks, metadata, lineage, classification, access, retention, privacy, and compliance. Lead cross-functional stakeholders, establish governance operations, build stewardship capability, communicate with executives, and drive organizational change. The role supports cloud data platform modernization, self-service analytics, and AI enablement within a property and casualty insurance organization.

Design and lead scalable data architecture and orchestration systems supporting AI infrastructure and digital marketing platforms. Build production-ready batch and streaming pipelines, transform structured and unstructured data, and integrate sources with Snowflake. Apply sharding, replication, indexing, normalization, and data governance best practices. Develop tools for data scientists and analysts, collaborate cross-functionally, document architectural solutions, and guide teams in delivering secure, reliable, production-ready AI data systems.

Supervise data engineers developing and maintaining enterprise data products for analytics, AI, machine learning, and business intelligence. Responsibilities include team leadership, contractor oversight, governance and change management, resource planning, vendor management, architecture road mapping, incident support, and employee coaching. The role also supports cloud and lakehouse implementations, data quality practices, Agile and DevOps processes, and operational efficiency across multiple concurrent projects.

Design and build scalable data engineering and AI agent governance capabilities across major cloud AI platforms. Develop ingestion, processing, ETL, observability, security, lifecycle management, and identity governance features. Evaluate emerging AI agent frameworks and interoperability standards, troubleshoot customer issues, document architecture and processes, and collaborate with engineering, security, compliance, product, and support teams. The role also involves improving distributed data systems, mentoring engineers, and leading technical initiatives.