Top Senior Data & Analytics Jobs

Lead enterprise data architecture across warehouses, data lakes, downstream applications, and AI workloads. Design data models, Medallion Architecture, ELT standards, semantic layers, APIs, access controls, and reusable datasets. Partner with business and BI teams to drive adoption, conduct architecture reviews, establish platform standards, and mentor data and analytics engineers. The role requires deep Snowflake, dbt, data modeling, pipeline, and semantic-layer expertise, with experience in AI data patterns and cloud infrastructure preferred.

Owns data governance and lifecycle management for Wealth Management. Defines data requirements, metadata, classifiers, quality standards, and risk processes; maintains a data-product backlog; investigates data issues; partners with technology and business teams; supports data publishing and governance initiatives; and mentors junior staff.

Lead data architecture and data-product design for wealth management domains. Define conceptual, logical, and physical models; establish semantic layers, metadata, ontologies, taxonomies, governance standards, and interoperability patterns. Apply AI to automate metadata creation while maintaining human oversight and governance. Ensure data products are AI-ready through stable identifiers, entity resolution, historical modeling, and clear semantics. Partner with technical and business stakeholders to achieve target-state architecture across cloud, warehouse, lakehouse, API, and BI platforms.

Owns data governance and management procedures supporting business operations and advanced analytics. Defines data requirements, metadata, classifiers, and quality standards; manages data-product backlogs; monitors lifecycle risks; investigates data issues; and partners with technology and business teams on remediation and compliance. The role also supports data publishing standards, risk management, and junior staff development.

Build Affirm’s narrative intelligence function by analyzing audience, behavioral, brand, media, survey, and business data. Develop narrative measurement frameworks, dashboards, analytical standards, and feedback loops to assess how effectively communications resonate. Design rigorous research studies, integrate qualitative and quantitative evidence, identify meaningful patterns, and translate findings into positioning, messaging, campaign, content, and communications strategy. Partner across analytics, research, marketing, creative, and communications teams to turn complex evidence into actionable decisions.

Designs and maintains reusable data ingestion and transformation components, automated quality and governance checks, reliable pipelines, monitoring, deployment automation, cloud integrations, containers, and infrastructure as code. Partners with data consumers on usable models and datasets, documents standards and runbooks, supports Agile improvement, and responds to critical IT incidents. Requires 5+ years of data engineering experience, orchestration, distributed processing, storage, scripting, automation, and version control expertise.

Designs and implements reusable data ingestion and transformation components, automated quality and governance checks, reliable pipelines, monitoring, alerting, and observability practices. Supports CI/CD, cloud integrations, container orchestration, infrastructure as code, deployment automation, data modeling, documentation, and Agile platform operations. The role also contributes to incident response and critical escalations.

Lead a distributed team of five Data Integration Engineers supporting client integrations. Oversee complex data flows, endpoint design, data quality, validation, and integration escalations. Establish priorities across client commitments and business goals, improve onboarding speed and quality, and develop shared tools, processes, automation, and AI-driven validation workflows. Collaborate with Operations, Sales, Product, Development, and Data Science teams while serving as a technical and strategic advocate for clients.

Leads the architecture, administration, engineering, and strategic evolution of enterprise Hadoop-based data platforms. Owns cluster lifecycle, scalability, reliability, security, disaster recovery, monitoring, optimization, upgrades, and operational standards. Troubleshoots distributed systems, supports data ingestion and governance, guides hybrid cloud strategies, documents platform operations, and mentors engineering teams. The role also develops data views, exports, and automated processing while collaborating with data engineering, analytics, security, and infrastructure teams.

Analyzes business challenges and opportunities for Capital One Shopping, developing strategic recommendations, product and pricing models, credit risk models, marketing insights, and monitoring plans. Leads cross-functional initiatives with IT, Sales, Operations, and Finance to improve growth, profitability, customer experiences, and credit performance. Requires strong quantitative analysis, communication, business judgment, and execution skills.

Leads enterprise analytical tool validation, assesses methodology quality and risk, researches emerging analytics practices, executes validation projects, communicates findings, and mentors junior staff. Partners cross-functionally to influence business strategy and develops solutions to complex data and business problems.

Analyze AML investigation volumes, risks, productivity, capacity, KPIs, and financial contributions. Develop analytical recommendations, optimize investigative operations, support modeling and strategy, and lead implementation and monitoring efforts. Partner with IT, Sales, Operations, Finance, and other teams to improve quality, service, volume, and profitability while supporting AML defenses and law-enforcement investigations.

Administer, optimize, and modernize enterprise SQL Server, Azure SQL, MongoDB, and MongoDB Atlas environments. Responsibilities include performance tuning, high availability, disaster recovery, security, monitoring, migrations, automation, capacity planning, architecture guidance, and rotational on-call support. The role also leads database projects and adopts AI-assisted engineering solutions to improve reliability and operational efficiency across mission-critical healthcare applications.

Designs and leads scalable Azure data platforms, ETL/ELT pipelines, Spark workflows, AI integrations, and cloud applications. Responsibilities include data modeling, advanced SQL optimization, reliability improvements, API integration, AI-powered automation, architectural standards, stakeholder analysis, mentoring, and evaluating emerging technologies. The role uses Azure Databricks, Snowflake, Spark, Python, and modern software engineering practices to support analytics, machine learning, business intelligence, and healthcare data solutions.

Leads the GTM analytics roadmap, modernizes spreadsheet reporting into scalable products, establishes trusted metrics and data models, and builds self-service and AI-assisted analytics. Partners cross-functionally with Sales, Marketing, Customer Success, Finance, Data Engineering, and Systems to improve pipeline conversion, productivity, retention, expansion, and planning. The role requires hands-on analytical product development, executive communication, data governance, and influence without formal authority.

Own supervisory data management and governance as a subject matter expert. Define data quality rules, metrics, monitoring, issue remediation, taxonomies, metadata, and cataloging practices. Partner with business, IT, and platform teams to improve data reliability, accessibility, lifecycle management, and future AI readiness. Translate business needs into technical requirements, facilitate adoption of governance practices, and communicate recommendations to technical and executive audiences.