Team Leadership & Talent
● Lead the team. Manage a talented team of senior and junior developers, analysts, and data scientists — setting direction on best practices, engineering standards, and alignment with team priorities.
● Develop people. Mentor team members, connecting their personal and professional goals to real career growth; set the standard for rigor and craft.
Stakeholder Partnership & Requirements
● Be the trusted partner. Serve as the primary interface to the different organizations and run structured discovery to elicit underlying needs, not feature requests.
● Write requirements that hold up. Translate business questions into testable specs: source systems, grain, metric definitions, SLAs, quality thresholds, and acceptance criteria. Drive a single source of truth for core metrics and arbitrate definitional conflicts.
● Simplify the complex. Navigate ambiguous, politically loaded problems and distill them into actionable messaging that aligns with business priorities and lands with executives. Prioritization,
Roadmap & Datamart Strategy
● Own the roadmap. Manage, prioritize, and align data, reporting, and analytics requirements with business needs, overseeing development and implementation.
● Make trade-offs explicit. Apply a consistent prioritization framework — value, reach, effort, risk, strategic fit — and communicate the rationale transparently, including to stakeholders you deprioritize.
● Shape strategy at the table. Play a key role in cross-functional committees defining data mart strategy, governance, and policy; invest ahead of demand in capabilities that make future requests cheaper to serve.
Visualization & Experience Design
● Design for the decision. Set the standard for how analytics is presented — choose the right visual for the question, build a clear hierarchy, and design every dashboard around the decision the user is trying to make rather than the data that happens to be available.
● Own the design system. Define and enforce visualization standards across the portfolio — chart conventions, color, typography, layout, naming, interaction patterns, and accessibility — so products look and behave consistently and stay maintainable as the team grows.
● Prototype, test, and prune. Wireframe and iterate with real users before building; validate with usage data and feedback, and retire dashboards that don't drive action.
Technical Leadership: Warehouse, BI & Pipelines
● Lead technically. Hands-on in advanced SQL on Snowflake, with the credibility to review the team's work rather than take it on faith.
● Design for scale. Oversee scalable pipelines and workflows that process large, diverse, unstructured datasets with high performance and reliability; automate cleaning, integration, and analysis in SQL and Python.
● Enable self-service. Champion governed datasets, semantic layers, and intuitive dashboards that let non-technical users answer their own questions — over one-off extracts and bespoke reports.
● Optimize what exists. Drive innovation in data architecture, dashboard performance, and cost efficiency; champion data quality and accuracy through lineage, testing, and clear ownership.
Data Science, AI & Machine Learning
● Set the AI agenda. Identify, size, and prioritize the use cases where ML and generative AI create real business value — and say no to the ones that don't.
● Deliver GenAI experiences. Build generative AI into the analytics stack — natural-language querying, conversational BI, automated insight generation, AI-assisted documentation and discovery.
Adoption, Measurement & Communication
● Measure what matters. Define success metrics for every product — adoption, time-to-insight, data freshness, decisions enabled, cost — and hold the team accountable to them.
● Drive adoption and clarity. Lead enablement through documentation, training, and change management; present insights, risks, and trade-offs to audiences from individual engineers to the executive staff.