Top Senior Data & Analytics Jobs

Develop scalable analytics assets and serve as the Power BI/Microsoft Fabric expert. Build and publish trusted datasets, semantic models, and dashboards; author Databricks workflows and production SQL; apply R for statistical analysis; ensure governance, versioning, and reproducibility; partner with stakeholders and Data Engineering to deliver high-impact insights, define KPIs, and communicate executive-ready recommendations.

Analyze behavioral, transactional, and interaction data to identify fraud patterns and emerging threats. Develop monitoring frameworks, dashboards, and mitigation solutions; evaluate AI fraud-review decisions; and translate findings into prompt, rule, workflow, and machine-learning feature improvements. Collaborate with Fraud Operations and Machine Learning teams, participate in code reviews, and mentor junior analysts. The role requires strong SQL and Python skills, statistical analysis expertise, business judgment, and fraud analytics experience.

Leads commercial data governance strategy, operating models, policies, standards, ownership, stewardship, metadata, lineage, and data-quality programs. Partners with cross-functional stakeholders to improve data integrity, accessibility, privacy, compliance, and responsible use. Oversees governance councils, catalogs, glossaries, issue remediation, performance metrics, tool adoption, training, and change management across a regulated biotechnology commercial organization.

Conduct quantitative research for systematic FX options and volatility strategies. Develop forecasting models, trading signals, market-making approaches, and systematic hedging strategies. Own the full research lifecycle, including data analysis, feature engineering, backtesting, validation, portfolio construction, performance attribution, stress testing, and live deployment. Collaborate closely with traders and engineers, build analytical tools, monitor production performance, and communicate research findings, assumptions, and risks to senior stakeholders.

Lead operations research and data analytics supporting Department of War acquisition, sustainment, workforce, and decision-analysis initiatives. Develop analytical products, predictive models, dashboards, business intelligence solutions, reports, data frameworks, and quality documentation. Collaborate with government leaders and cross-functional stakeholders to analyze disparate datasets, streamline reporting, and provide data-driven findings and recommendations.

Manage and evolve a cloud-native enterprise data platform across Azure, Databricks, Unity Catalog, Collibra, and Immuta. Optimize platform performance, security, scalability, observability, access, workloads, and cost. Design and maintain lakehouse architectures, batch and streaming pipelines, integrations, governance controls, and data-quality processes. Automate infrastructure and deployments using infrastructure as code and CI/CD. Lead incident response, upgrades, resilience activities, technical documentation, design reviews, and coaching while collaborating with engineering, security, architecture, governance, and business teams.

Leads and manages a technical team building and operating enterprise-scale GCP data pipelines. Oversees ingestion, transformation, curation, exposure, governance, reliability, cost optimization, and migration of data workloads. Provides hands-on technical leadership in SQL, Python, Spark, data modeling, and GCP architecture while partnering with architects, platform teams, governance stakeholders, and business users. The role also manages operational metrics, compliance requirements, emergency assignments, and team development.

Leads audit analytics across the full audit lifecycle, including risk-based testing, continuous auditing, reporting, dashboards, and data visualization. Advises audit teams and leadership on healthcare data, Epic systems, AI governance, automation, and AI-enabled audit solutions. Evaluates AI risks involving data integrity, bias, explainability, controls, ethics, and compliance. Provides mentoring, supports complex healthcare audits, collaborates with data owners and IT partners, and advances analytics methodologies and standards.

Leads retrospective analysis of wearable sensor and physiological data, developing analysis tools, metadata systems, visualizations, statistical models, and machine learning workflows. Investigates data anomalies across hardware-to-cloud systems, curates large heterogeneous datasets, defines analytical requirements, and translates complex data into products and reports for technical, executive, and end-user audiences. Collaborates with engineering, product, and business stakeholders while establishing analytical standards and tooling.

Own Moxie’s end-to-end data infrastructure, including pipelines, Snowflake warehousing, dbt and Omni modeling, orchestration, observability, and documentation. Partner with Engineering and Finance on schema changes, accounting logic, metrics, and business-critical reporting. Deliver financial or historical EMR data models, establish SOPs and reliability KPIs, optimize performance and cost, and help advance Practice Intelligence toward predictive and prescriptive products with appropriate AI safeguards.