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Key Responsibilities and Required Skills for Analytics Lead

💰 $ - $

Data & AnalyticsProductBusiness IntelligenceLeadership

🎯 Role Definition

The Analytics Lead is a senior individual contributor or front-line manager who designs and delivers actionable analytics, defines measurement strategy, and partners with product, marketing, finance, and engineering to translate business questions into data products. This role combines technical proficiency (SQL, Python/R, BI tooling) with strong stakeholder management, coaching, and product-oriented thinking to increase revenue, reduce costs, and optimize key business metrics through data—making it ideal for candidates experienced in analytics strategy, data modeling, experimentation, and cross-functional influence.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Senior Data Analyst with demonstrated ownership of reporting and analytics products
  • Product Analyst or Business Intelligence (BI) Analyst with cross-functional partnerships
  • Analytics Manager or Data Science Senior Associate who owns end-to-end analytics initiatives

Advancement To:

  • Head of Analytics / Director of Analytics
  • Director of Business Intelligence or Director of Product Analytics
  • VP of Data, Chief Data Officer (CDO) or Senior Director of Data & Insights

Lateral Moves:

  • Product Analytics Lead
  • Data Science Manager
  • BI Engineering Lead / Data Engineering Partner

Core Responsibilities

Primary Functions

  • Lead the analytics vision and roadmap: define short- and long-term analytics priorities aligned to business objectives, translate company goals into measurable OKRs, and own delivery of analytics products (dashboards, cohorts, funnels, attribution models) that drive decision-making across leadership teams.
  • Build and maintain end-to-end reporting and dashboard solutions using SQL, Looker/Tableau/Power BI, and modern data warehouses (Snowflake, BigQuery) to provide self-serve insights for product, growth, finance, and operations stakeholders.
  • Design and implement scalable measurement frameworks and instrumentation (event tracking, product telemetry, analytics schema) in partnership with product and engineering to ensure consistent, reliable data capture for experiments and analytics.
  • Own metric definitions and governance: establish a single source of truth for KPIs, maintain a metrics catalog/glossary, manage semantic modeling (dbt, LookML), and enforce versioning, lineage, and access controls.
  • Lead cross-functional analytics projects: translate business questions into analysis plans, develop hypotheses, build statistical tests or predictive models, and deliver concise recommendations tied to business impact.
  • Drive experimentation and A/B testing strategy: partner with product and engineering to design experiments, analyze results with rigorous statistical methods, compute power/sample size, and convert learnings into product changes.
  • Mentor, coach, and grow a team of analysts: provide technical and career guidance, conduct code and analysis reviews, set performance goals, and promote best practices in SQL, analysis reproducibility, and data storytelling.
  • Partner with finance and commercial teams to develop revenue and cost models, pricing elasticity analysis, CLTV and CAC measurement, and financial forecasting that informs investment and go-to-market decisions.
  • Create advanced analytics solutions when required: build predictive models (churn, propensity, segmentation) and work with data science to productionize models or convert them into scoring pipelines.
  • Translate complex analyses into clear narratives and executive-ready presentations, combining quantitative rigor with compelling visualizations and business recommendations.
  • Optimize analytics infrastructure and processes: identify performance bottlenecks, recommend ETL/ELT improvements, prioritize data engineering work, and help implement CI/CD, testing, and monitoring for analytics pipelines.
  • Monitor product and business health through proactive anomaly detection, alerting, and root-cause diagnosis of KPI shifts, conversion drops, or revenue changes.
  • Lead stakeholder engagement and prioritization of analytics backlog: run intake processes, negotiate scope and SLAs, and ensure analytics work is aligned with strategic initiatives.
  • Manage vendors and external partners for BI tools, tracking platforms, or consulting engagements, including requirements, SOWs, and vendor performance evaluation.
  • Ensure compliance with data privacy and regulatory requirements (GDPR, CCPA) by partnering with legal, privacy, and security teams to implement safe data access patterns and anonymization where necessary.
  • Develop and maintain cohort analyses, lifetime value models, retention curves, and funnel analyses that inform product roadmaps and marketing strategies.
  • Collaborate with product managers and designers to identify measurement opportunities in new features, ensure analytics acceptance criteria are met, and validate feature launches with rapid post-launch analyses.
  • Provide thought leadership on analytics best practices and tooling: evangelize self-serve analytics, run internal analytics training, and create documentation and templates to raise analytical maturity across the organization.
  • Lead cross-functional post-mortems for major incidents or performance degradations, conducting forensic analytics to recommend remediation and controls to prevent recurrence.
  • Quantify and communicate the business impact of analytics initiatives (improvements in conversion, retention, revenue uplift, cost savings) and track ROI of analytics investments.
  • Establish and track SLAs for analytics deliverables and maintain high quality of analyses through peer review, automated tests, and reproducible notebooks or pipelines.
  • Serve as an escalation point for complex ad-hoc requests from executive stakeholders, synthesizing multiple data sources into a coherent answer and rapid decision support.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.
  • Create reusable analysis templates, notebooks, and visualization components to speed up delivery and standardize insights.
  • Help define and maintain data access policies, roles, and permissions for analytics users.
  • Facilitate knowledge sharing sessions, brown-bags, and internal workshops to uplift analytics literacy across teams.
  • Assist in recruiting, interviewing, and onboarding new analytics hires to scale the analytics capability.
  • Maintain up-to-date documentation (metrics catalog, data lineage diagrams, analysis playbooks) to ensure long-term maintainability of analytics assets.
  • Track industry analytics trends, tooling innovations (data mesh, reverse ETL) and recommend adoption where it creates strategic value.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL: complex joins, window functions, CTEs, performance tuning for large datasets — daily usage for reporting and analysis.
  • BI visualization tooling: expert-level experience building dashboards and data models in Looker (LookML), Tableau, or Power BI with emphasis on interactivity and performance.
  • Data warehousing and cloud platforms: practical experience with Snowflake, BigQuery, Redshift, or similar modern data warehouses and familiarity with ELT patterns.
  • Data modeling and semantic layer design: building star schemas, dimensional models, logical metrics, and reusable business logic (dbt, LookML).
  • Programming for analytics: Python or R for advanced analysis, scripting, ETL tasks, and model prototyping (Pandas, NumPy, SciPy, scikit-learn).
  • Experimentation & statistics: A/B test design, hypothesis testing, power analysis, p-values, confidence intervals, false discovery rate control, and causal inference basics.
  • Analytics engineering & pipeline tooling: familiarity with dbt, Airflow, Dagster, or similar orchestration tools and best practices for version control and CI.
  • Product and growth analytics techniques: funnel & cohort analysis, retention modeling, attribution, lift analysis, and LTV/CAC computations.
  • Data governance & privacy: understanding of data lineage, data cataloging, role-based access control, and regulatory compliance (GDPR/CCPA).
  • Cloud analytics ecosystem: experience integrating with marketing analytics, instrumentation platforms (Segment, Snowplow), and reverse ETL tools (Hightouch).
  • Advanced spreadsheet modeling: Excel/Google Sheets for rapid prototyping, sensitivity analysis, and stakeholder-ready financial modeling.
  • Basic machine learning familiarity: understanding model evaluation metrics, productionization considerations, and collaborating with data science to deploy models.

Soft Skills

  • Strong stakeholder management: ability to influence senior leaders, negotiate trade-offs, and align analytics priorities with business goals.
  • Excellent communication and storytelling: distill complex analyses into concise, action-oriented recommendations for executives and non-technical audiences.
  • Strategic thinking and business acumen: translate data into product and commercial strategy, driving measurable outcomes.
  • Coaching and people leadership: mentor analysts, provide developmental feedback, and foster a culture of analytical rigor and curiosity.
  • Problem-solving and critical thinking: structure ambiguous problems, identify root causes, and propose practical, testable solutions.
  • Prioritization and project management: manage multiple analytics projects, set realistic timelines, and deliver high-impact insights against deadlines.
  • Cross-functional collaboration: work effectively with product managers, engineers, marketers, finance, and legal to deliver integrated solutions.
  • Attention to detail and quality orientation: ensure accuracy, reproducibility, and clarity in all analyses and dashboards.
  • Adaptability and learning mindset: stay current with analytics tooling, methodologies, and industry best practices.
  • Ethical judgment: make responsible choices about data usage, consent, and privacy in analytical work.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Statistics, Mathematics, Economics, Business Analytics, Data Science, or a related quantitative field.

Preferred Education:

  • Master's degree or higher (MS, MSc, MBA, or PhD) in Data Science, Statistics, Economics, Operations Research, or similar; or equivalent industry experience.

Relevant Fields of Study:

  • Computer Science
  • Statistics
  • Data Science / Analytics
  • Economics
  • Mathematics
  • Business Analytics / Operations Research

Experience Requirements

Typical Experience Range: 5–12 years in analytics, BI, data science, or related roles with progressively increasing responsibility and ownership.

Preferred:

  • 7+ years of hands-on analytics experience with at least 2 years in a lead or people-management role (or equivalent technical lead capacity).
  • Proven track record of delivering analytics products that materially influenced product, marketing, finance, or operations decisions.
  • Experience working in fast-paced, cross-functional product organizations and managing stakeholder expectations across executive teams.
  • Industry experience may be preferred depending on employer (e.g., SaaS, e-commerce, fintech, adtech, healthtech).
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