Manager - Business Intelligence - Fintech

About the Role:

We are looking for a strategic, hands-on Manager - Business Intelligence to lead our BI team and own the delivery of trusted, scalable reporting.

The role combines deep expertise in data modelling, data warehousing (Amazon Redshift and MongoDB), ETL (Python, Pentaho, and Spark), and Tableau with strong people leadership: defining KPIs and the BI roadmap with business leaders, embedding data quality controls, using AI to speed up delivery, and growing a team with deep functional expertise and future leaders.

Key Responsibilities:

Leadership & People:

- Lead, mentor, and manage a team of BI analysts, covering hiring, goal setting, performance reviews, and day-to-day coaching.

- Groom next-level leaders by identifying high-potential team members, defining individual career plans with clear competencies, milestones, and stretch assignments, and maintaining succession plans.

- Manage conflict within the team and across stakeholder groups, resolving competing priorities, disagreements, and escalations constructively to maintain trust and delivery momentum.

- Build functional expertise in core business processes (lending lifecycle, originations, collections, risk, finance, and marketing) so the team understands the business context behind every metric.

Strategy & Stakeholder Delivery:

- Define and own the BI roadmap, aligning reporting and analytics priorities with business objectives.

- Partner with business leaders to gather requirements, define KPIs, and translate them into scalable reporting solutions, and present insights and recommendations to senior management.

- Plan, prioritize, and track delivery of dashboards, reports, and ad hoc analyses using Agile/Scrum (sprint planning, backlog grooming, and reviews), managing scope, timelines, and SLAs.

Data Modelling, Engineering & AI:

- Own BI data model design on Amazon Redshift and MongoDB, applying dimensional and galaxy (fact constellation) modelling, and review complex SQL for performance.

- Give structured feedback to the data warehouse team on metric definitions and data model design, including grain, conformed dimensions, fact table design, and gaps found during reporting, so the warehouse evolves with reporting needs.

- Oversee ETL pipelines built with Python, Pentaho, and Spark for reliability, monitoring, and timely refreshes, and scale them cost-effectively on AWS using EC2 Spot Instances, EMR, autoscaling, and parallel processing.

- Use AI (LLMs, AI coding assistants, and agentic workflows) to automate ETL development and report building for new reporting lines, including SQL/Python code, source-to-target mappings, Tableau calculations, documentation, and test cases, with human review and validation.

- Oversee the development of interactive dashboards and reports in Tableau and AI-powered visualization tools (e.g., Tableau AI, Power BI Copilot, or similar), using features such as natural-language queries and automated insights, and manage Tableau Server/Cloud administration, deployments, and usage monitoring.

Data Quality, Governance & Standards:

- Embed data quality and reconciliation controls on reports, including source-to-target count and amount reconciliations and completeness, freshness, threshold, and variance checks, with automated alerts and sign-off before publication.

- Build and manage a semantic layer as the single source of truth for business metrics, with governed definitions, a data dictionary, and documented business logic, ensuring consistent KPIs across Tableau, self-service, and AI-assisted analytics.

- Enforce BI standards for dashboard design, SQL coding, documentation, code review, version control (Git/Bitbucket), and release management.

- Ensure data security and access control, including role-based and row-level security and protection of sensitive customer data (PII), and support audit and regulatory reporting needs.

- Lead root-cause analysis and resolution of critical data discrepancies and production issues, and drive self-service analytics and reporting automation to reduce manual effort.

Required Skills & Qualifications:

- Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, Statistics, or a related field.

- 8 - 10 years of experience in BI, data analytics, or data warehousing, including at least 2 - 3 years leading or managing a team.

- Strong command of data modelling techniques, including dimensional (Kimball), normalized/3NF (Inmon), and Data Vault, and when to apply each.

- Hands-on implementation of galaxy schema (fact constellation) models with conformed dimensions shared across multiple fact tables, alongside star and snowflake schemas.

- Proven experience building data warehouses end to end, from source analysis and modelling through ETL, loading, and reporting, preferably on Amazon Redshift, with hands-on MongoDB experience.

- Advanced SQL, including window functions, CTEs, query optimization, and performance tuning.

- Hands-on ETL development with Python, Pentaho, and Spark (PySpark), including advanced Python libraries (e.g., Pandas, Polars, Dask, boto3) and scaling pipelines on AWS Spot Instances.

- Expert-level Tableau Desktop and Tableau Server/Cloud skills, including LOD expressions, performance optimization, and administration.

- Practical experience using AI to automate ETL pipelines and report development.

- Experience implementing data quality and reconciliation controls for business-critical reports.

- Demonstrated people leadership, including conflict management, mentoring, career planning, and grooming team members into leadership roles.

- Strong stakeholder management and communication skills, with the ability to translate business problems into analytical solutions and influence senior leadership.

Preferred Qualifications:

- Experience with Apache Iceberg and Amazon EMR (added advantage).

- Experience building and managing a semantic layer (e.g., governed metrics or dbt).

- Experience with workflow orchestration (e.g., Apache Airflow) and AWS cost optimization.

- Domain experience in financial services, lending, or fintech, and exposure to data governance, cataloguing, and lineage tools.

- Tableau certification (e.g., Tableau Certified Data Analyst or Server Certified Associate).

Skills

Analytics, Data Analytics, Business Intelligence, Data Modeling, Data Visualization, SQL, Python, Tableau, Data Management, Data Warehousing