Top Data & Analytics Jobs

Designs and maintains scalable data pipelines and platform architecture using Python, dbt, Airflow, and Snowflake. Responsibilities include data modeling, ELT development, quality and observability, production debugging, source-data validation, performance optimization, and Tableau reporting. The role leads ad hoc analysis, collaborates with engineering, marketing, product, and leadership teams, makes architectural decisions, and mentors colleagues through code reviews and knowledge sharing.

Partner with commercial and delivery teams to build scalable reporting, KPIs, dashboards and forecasts. Analyse sales, pre-sales and delivery performance to reduce time-to-revenue, improve deal velocity, and provide data-driven recommendations to drive commercial growth.

Coordinates day-to-day data enablement activities, including data entry, maintenance, cleansing, standardization, quality audits, issue resolution, user support, process documentation, and reporting on data quality metrics. Works with data managers and department partners to keep organizational data accurate, accessible, and usable while supporting data tools and workflows.

Leads a client-facing healthcare analytics team that designs and maintains prior authorization decision automation programs. Consults PaaS clients, develops strategic roadmaps, translates requirements into technical blueprints, analyzes authorization and claims data, and builds KPI reports using visualization tools. Manages direct reports, improves analytics processes and AI tooling, partners with product and engineering teams, and ensures automation outcomes and client KPIs are achieved.

Lead analytics engineering by developing scalable data models, defining analytics requirements, establishing company-wide data quality standards, and delivering reliable data products. Partner with analysts, engineers, product managers, and other stakeholders to improve data accessibility and decision-making. The role requires advanced SQL, Snowflake, dbt, BI tools, data modeling, testing, documentation, version control, and CI/CD experience, along with fintech industry knowledge.

Build and maintain ETL/ELT pipelines to move product data into analytics-ready stores (Postgres, data lake, warehouse). Design and optimize data models, ensure data quality and documentation, support ad-hoc research requests, and collaborate with research and engineering teams to enable reproducible analytics.

Conduct quantitative equity research using financial statements, holdings, company filings, and varied datasets. Develop valuation, business analysis, classification, narrative, and portfolio analytics; validate models before launch and investigate production issues. Document code and methodologies, review peer work, identify data-quality issues and edge cases, and communicate analytical findings to senior colleagues. The role requires strong equity fundamentals, Python and SQL expertise, rigorous research practices, and familiarity with AI-assisted research and validation.

Own data pipelines into and out of the warehouse, dbt models, data integrity, governance, identity resolution, reverse ETL, and quality monitoring across growth, lifecycle, and brokerage data. Partner with product, marketing, and operations teams to establish data contracts, ensure accurate and timely business metrics, and support regulated financial data requirements.

Build and operate governed Finance data infrastructure on Databricks, including scalable pipelines, integrations, semantic layers, data-quality controls, observability, and AI-enabled services. Partner with analytics and enterprise teams on architecture, data contracts, security, recoverability, and production ownership. Optimize platform costs through FinOps practices and use AI coding assistants while validating performance, correctness, and security.

Leads a team of data scientists and BI engineers responsible for demand forecasting, customer routing analytics, budgeting projections, and core business KPIs. Designs scalable Snowflake datasets, metric definitions, and Tableau self-service analytics; partners with Product, Engineering, and Finance; establishes analytical strategy and roadmaps; applies statistical and causal reasoning; and guides responsible AI-assisted analytics practices.