Analytics Engineering Team Lead

About the role:

The Team Lead, Analytics Engineering is responsible for defining and leading Bank X's analytics layer, transforming enterprise data into trusted, reusable, governed, and AI-ready business data products. This role bridges Data Engineering, AI Engineering, Product, Risk, Finance, Operations, Regulatory Reporting, and executive stakeholders.

As Bank X evolves into an AI-native bank, this role is accountable for building the bank's Business Knowledge Layer, enabling both human users and AI agents to access consistent definitions, metrics, Customer 360 assets, and curated analytical datasets.

Key Responsibilities:

Analytics Product Strategy

• Define and execute the analytics engineering strategy aligned with Bank X's business strategy, AI roadmap, and regulatory priorities.

• Establish the vision, standards, and operating model for analytics data products across the bank.

• Drive the evolution from one-off reporting datasets toward reusable business data products and AI-ready information assets.

• Lead the roadmap for strategic views such as Customer 360, Product 360, Risk 360, Finance views, and operational intelligence assets.

Semantic & Metrics Governance

• Own the enterprise semantic layer and business metric framework.

• Define, standardize, and govern KPI definitions, dimensions, business rules, and calculation logic across domains.

• Ensure consistency of metrics across executive reporting, regulatory reporting, dashboards, self-service analytics, AI applications, and agentic workflows.

• Act as a steward of the analytical truth layer for both human decision-makers and AI systems.

AI & Data Product Enablement

• Partner with AI Engineering and Product teams to design data products optimized for AI consumption.

• Enable trusted AI-ready datasets for AI Customer Service, AI Money Coach, personalization, customer insights, internal AI agents, and future AI-native banking capabilities.

• Ensure data assets are discoverable, reusable, documented, governed, and suitable for retrieval, reasoning, recommendation, and decision support.

• Prevent fragmented AI datasets by promoting reusable and governed analytical foundations.

Team Leadership & Engineering Excellence

• Lead, coach, and mentor Analytics Engineers, setting clear priorities, capability expectations, and career development paths.

• Establish development standards for SQL, data modeling, semantic design, testing, documentation, code review, and release management.

• Create a culture of data product ownership, business understanding, quality, speed, and continuous improvement.

• Balance hands-on technical guidance with stakeholder leadership and roadmap ownership.

Stakeholder Partnership

• Serve as the primary analytics engineering partner for Product, Business, Finance, Risk, Operations, Regulatory Reporting, and AI & Data teams.

• Translate business priorities into scalable analytical assets and influence stakeholders toward standardized definitions.

• Resolve metric conflicts and drive alignment on business logic across domains.

• Communicate roadmap, trade-offs, dependencies, and delivery progress clearly to senior stakeholders.

Data Quality, Governance & Trust

• Define data quality monitoring, validation, observability, and reconciliation practices for critical analytical data products.

• Ensure compliance with data governance, security, privacy, consent, and banking regulatory expectations.

• Promote transparency through metadata, lineage, ownership, and documentation standards.

• Drive continuous improvement in trust, usability, and adoption of analytical assets.

Qualifications:

Experience

• 7+ years of experience in Analytics Engineering, Data Engineering, BI Engineering, Data Warehousing, or related disciplines.

• 2+ years of experience leading analytics, BI, data, or engineering teams.

• Experience defining enterprise data models, semantic layers, reusable data products, and metric governance frameworks.

• Experience in banking, financial services, fintech, or regulated industries is strongly preferred.

Technical Expertise

• Advanced SQL, data modeling, dimensional modeling, and analytics architecture capability.

• Strong knowledge of semantic modeling, data marts, metadata management, lineage, governance, and data quality frameworks.

• Hands-on experience with platforms such as Microsoft Fabric, Power BI, dbt, Databricks, Snowflake, BigQuery, or similar modern data platforms.

• Ability to review technical designs and guide the team toward scalable, maintainable, and performant analytical solutions.

AI Tool Fluency - Minimum Requirement

• Minimum hands-on working knowledge of major AI assistants such as ChatGPT, Claude, and Gemini, with ability to compare strengths and limitations for analytics engineering work.

• Ability to coach the team on responsible use of AI tools for requirement analysis, SQL generation review, documentation, data testing, stakeholder communication, and productivity improvement.

• Strong understanding of prompt writing, AI output verification, confidentiality, data leakage risks, and appropriate use of Bank X-approved AI tools.

• Ability to identify opportunities where AI tools can accelerate analytics engineering delivery without compromising quality, governance, or regulatory expectations.