Top Data & Analytics Jobs

Leads site data, integration, and systems capabilities within a regulated manufacturing environment. Owns data strategy, governance, standards, system integrations, historians, data platforms, application lifecycle management, and AI enablement. Ensures compliant, reliable, contextualized manufacturing data while maintaining validation and inspection readiness. Manages the data enablement roadmap, resolves data quality and connectivity issues, supports analytics and AI deployments, and leads technical teams, contractors, and cross-functional stakeholders.

Build and maintain scalable data pipelines supporting reporting, analytics, and AI/ML use cases. Write production Python and SQL code, integrate data from APIs and other sources, establish data governance, implement quality monitoring and alerting, support production incidents, and document technical solutions. Collaborate with data scientists, engineers, and business partners to deliver reliable data infrastructure and insights.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and ETL/ELT workflows. Build trusted data products for analytics, automation, and AI agents; translate ambiguous business needs into scalable solutions; lead cross-team initiatives; ensure reliability and data quality; mentor engineers and analysts; and develop, evaluate, and monitor production LLM and agent systems using Python and SQL.

Leads the acquisition, integration, modeling, and development of data analytics products. Designs data lake and warehouse solutions, builds ETL pipelines, applies engineering and CI/CD practices, supports analytics and machine learning applications, and ensures data quality and regulatory compliance. Collaborates with technical and business stakeholders, contributes to agile delivery, leads code reviews, mentors junior engineers, and develops enterprise data flows that establish a reliable source of truth.

Leads the Federal Reserve Bank of Boston’s Data & Analytics department, setting strategy, architecture, governance, and multi-year technical roadmaps. Oversees self-service analytics, data infrastructure, pipelines, curated datasets, visualizations, and decision-support tools. Builds stakeholder partnerships, ensures responsible AI use, manages operational risks and controls, and develops a high-performing multidisciplinary team. Represents the function to executive leadership and collaborates across the Federal Reserve System.

Build and operate the Global Card team’s data foundation, including pipelines, schemas, data marts, feature pipelines, quality monitoring, and data contracts. Lead architecture and technical direction, partner with product, analytics, and data science teams, contribute to backend services, establish engineering standards, mentor colleagues, and ensure reliable, secure, scalable data systems.

Administer and optimize P21/SQL databases, design logical and physical data models, manage SSRS reports and SSIS packages, ensure security, backups, and recovery, tune performance, collaborate with cross-functional teams and vendors, and document database procedures and policies.

Build and maintain scalable data models, pipelines, ETL workflows, and streaming solutions using dbt, PySpark, AWS Glue, Databricks, and Kafka. Apply software engineering practices including testing, version control, code reviews, and integration. Partner with data scientists, stakeholders, and data stewards to improve data quality, governance, accessibility, and reliability. Translate requirements into development plans and Jira stories while collaborating on data strategy, tooling, integrations, and deployments.

Build and maintain Databricks data pipelines using SparkSQL and PySpark; ingest, transform, model, and validate healthcare data from payer, provider, platform, and API sources. Support automated file exchanges, dashboards, downstream workflows, testing, governance, access controls, and audit logging. Participate in design and code reviews, use AI coding tools responsibly, and progressively own data engineering work while handling protected health information.

Lead strategy, architecture, and delivery of enterprise data engineering on the Data Cloud Platform. Design batch and real-time pipelines, event-driven streaming, data products, governance, lineage, quality, and observability. Provide technical leadership, mentor teams, drive adoption of standardized data models, CI/CD, and align platform capabilities with business and investment use cases.