Top Data & Analytics Jobs

Supports enterprise ESG data collection, validation, analysis, reporting, disclosures, assurance, and stakeholder requests. Develops dashboards and executive summaries, manages reporting systems and calendars, documents controls and methodologies, resolves data quality issues, coordinates internal and external assurance activities, researches sustainability standards, and contributes to materiality, scenario, and strategic ESG analyses.

Build and scale data pipelines, APIs, integrations, and data models supporting CRM personalization, segmentation, audience activation, and campaign measurement. Ensure pipeline reliability through monitoring, alerting, and logging; automate workflows; document systems; participate in architecture and code reviews; and mentor junior engineers. Collaborate with data engineering, data science, and CRM teams to improve personalized email, push, and SMS engagement for millions of users.

Leads marketing media measurement strategy across Social, TV, Influencer, and Content Marketing. Owns Marketing Mix Modeling, incrementality testing, causal inference, attribution, experimentation, and media effectiveness analysis. Uses SQL and statistical programming to evaluate customer and media data, develop investment recommendations, and communicate findings to executives. Partners cross-functionally with Marketing, Finance, Data Science, Engineering, Product, and agencies while leading analytics talent and remaining hands-on.

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.

Develop dashboards, data pipelines, forecasts, and scenario models for engineering leadership. Analyze resource utilization, headcount, budgets, and operational performance while applying AI/ML, LLMs, and automation to improve reporting and workflows. Translate complex, inconsistent data into actionable recommendations and identify process improvements across engineering operations.

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.