Key Responsibilities and Required Skills for Director of Analytics

🎯 Role Definition

The Director of Analytics is a senior analytics leader responsible for defining and executing an enterprise analytics strategy that informs business decisions, drives revenue and efficiency, and scales analytics capabilities across the company. This role blends people leadership, product-minded analytics, technical fluency in data platforms and BI tools, and strong cross-functional partnership with senior stakeholders across Finance, Product, Marketing, Operations, and Engineering.

📈 Career Progression

Typical Career Path

Entry Point From:

Senior Manager / Head of Analytics
Principal Data Scientist or Lead Business Intelligence Manager
Head of Business Analytics or Data & Insights Lead

Advancement To:

VP of Analytics / VP of Data & Insights
Chief Data Officer (CDO)
Head of Data Science & Analytics (executive leadership roles)

Lateral Moves:

Head of Business Intelligence / Head of Growth Analytics
Product Analytics Lead / Head of Customer Insights

Core Responsibilities

Primary Functions

Lead and own the enterprise analytics strategy, including defining measurable goals, KPIs, success metrics, analytics roadmaps, and resource plans to deliver business impact across revenue, retention, and operational efficiency.
Build, mentor and scale a high-performing analytics organization (managers, analysts, data scientists, analytics engineers) including hiring plans, career ladders, performance management, and a coaching culture focused on delivery and quality.
Partner with senior stakeholders (CRO, CFO, CPO, VP Marketing, VP Sales) to translate strategic business priorities into analytics programs and prioritize analytics initiatives by business value and ROI.
Design and implement end-to-end analytics solutions: data requirements, ETL/ELT pipelines, data modeling, BI dashboards, experimentation frameworks, and decision support tools that produce reliable, actionable insights.
Own analytics delivery and governance, establishing standards for data quality, lineage, cataloging, documentation, observability, reproducibility, and compliance with privacy/regulatory requirements.
Drive adoption of self-serve analytics by developing standardized metrics, semantic layers, BI templates, and training programs so business teams can make faster data-driven decisions.
Define and measure OKRs for the analytics function; use outcome-based metrics to demonstrate impact on business KPIs and iterate on analytics investments.
Oversee advanced analytics initiatives such as predictive modeling, propensity scoring, CLTV forecasting, uplift modeling, and personalization experiments in partnership with data science and product teams.
Shape the analytics technology stack: evaluate and select modern data platforms, cloud warehouses (Snowflake/BigQuery/Redshift), ETL tools, BI tools (Looker/Tableau/Power BI), feature stores, and MLOps solutions to support scale and latency requirements.
Translate complex analytical findings into clear, compelling narratives and executive-level presentations that inform strategy and operational decisions; act as a trusted advisor to executive leadership.
Lead cross-functional analytics projects including go-to-market analytics, pricing experiments, funnel optimization, retention analytics, financial planning & analysis (FP&A) support, and operational reporting.
Manage analytics budgeting, vendor relationships, contracts with BI and data platform providers, and internal allocation of analytics resources to meet quarterly and annual priorities.
Implement experimentation and A/B testing best practices (hypothesis design, randomization, power analysis, metrics selection) and partner with product/engineering to measure causal impact.
Institutionalize best practices for reproducible analytics: version control for analysis, peer reviews, standardized SQL/Notebooks, and automated data checks to reduce risk and increase trust.
Champion data privacy, security, and ethical use of analytics by working with legal, security, and compliance teams to enforce GDPR, CCPA, and internal controls on data usage.
Conduct regular analytics health checks, KPI audits, and metric harmonization across systems to eliminate stove-piped definitions and single-source-of-truth discrepancies.
Advocate for and lead cross-functional initiatives to embed analytics into product development, marketing campaigns, and sales enablement processes; operationalize insights into playbooks and dashboards.
Establish partnerships with data engineering to prioritize data ingestion, schema design, and performance tuning to ensure analytics queries and dashboards meet SLAs.
Drive continuous improvement in analytics velocity through process refinement, tooling, automation of repetitive reports, and enabling analytics engineers to focus on core business problems.
Oversee customer and market insights programs, segmentation analysis, and ad hoc research requests that guide product roadmap and go-to-market strategy.
Evaluate and operationalize machine learning models into production flows in collaboration with engineering and MLOps, ensuring monitoring, drift detection, and retraining processes are in place.
Lead change management and adoption efforts when rolling out new analytics platforms or metric definitions, including stakeholder communication plans and training workshops.
Manage high-visibility analytics programs tied to revenue or cost savings targets; create executive dashboards and regular business reviews to track performance and course-correct.
Serve as the analytics escalation point for complex cross-functional data issues, mediating priorities and resolving trade-offs between speed, accuracy, and scope.

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.
Act as an internal consultant to business teams, helping them frame analytical questions and design experiments that generate clear, actionable outcomes.
Maintain relationships with external analytics vendors, consultants, and academic partners to bring in best practices and supplemental expertise.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL expertise for complex joins, window functions, performance tuning, and production-grade analytics queries.
Strong experience with cloud data warehouses and modern data stack components (e.g., Snowflake, BigQuery, Redshift, Databricks).
Proficiency in BI and dashboarding tools such as Looker, Tableau, Power BI, Mode Analytics, or ThoughtSpot; ability to design executive-grade dashboards and semantic models.
Solid background in statistical analysis, experimental design, causal inference, and A/B testing methodologies.
Hands-on experience with analytics engineering tools and transformation frameworks (dbt, Airflow, Prefect) and knowledge of ELT/ETL patterns.
Familiarity with Python or R for advanced analytics, prototyping machine learning models, or automating tasks and data pipelines.
Experience operationalizing machine learning and productionizing models with MLOps practices (model monitoring, retraining, feature stores).
Data governance and metadata management knowledge, including cataloging, lineage, quality monitoring (Great Expectations, Monte Carlo, Datafold).
Experience defining and harmonizing metrics, semantic layers, and metric frameworks across multiple business systems.
Technical competency in API-based data integration, event-streaming and analytics for near-real-time use cases (Kafka, Kinesis, Segment).
Ability to read and critique code, SQL, and data pipelines to ensure analytics correctness and maintainability.
Experience with finance and business metrics, including revenue recognition, LTV, CAC, ARR/MRR, and cohort analysis.

Soft Skills

Strategic thinking and business acumen: translate analytics into measurable business outcomes and ROI.
Exceptional stakeholder management and executive communication skills; able to influence at C-suite level.
Leadership and people management: hiring, coaching, career development, performance feedback, and team culture building.
Problem-solving and decision-making under ambiguity; prioritize high-impact work.
Strong storytelling and data visualization skills to turn complex analysis into actionable recommendations.
Cross-functional collaboration and facilitation skills to align product, engineering, finance, and marketing.
Change management: drive adoption of new metrics, processes, and tooling across the organization.
Time management and project management skills, with experience delivering analytics projects on tight timelines.
Intellectual curiosity and continuous learning mindset; stay current with analytics trends and emerging technologies.
Coaching and mentorship: grow analytics capability across the organization through training and hands-on mentorship.

Education & Experience

Educational Background

Minimum Education:

Bachelor’s degree in a quantitative field (Statistics, Mathematics, Economics, Computer Science, Engineering, or related).

Preferred Education:

Master’s degree or MBA with quantitative emphasis, MS in Data Science, Statistics, Applied Math, or relevant technical graduate degree.

Relevant Fields of Study:

Data Science
Statistics / Applied Mathematics
Computer Science / Software Engineering
Economics / Finance
Business Analytics / Operations Research

Experience Requirements

Typical Experience Range: 8–15+ years in analytics, data science, or business intelligence roles with progressive leadership responsibilities.

Preferred:

10+ years of analytics experience with at least 3–5 years leading analytics teams in fast-paced, product- or growth-oriented companies.
Proven track record of building analytics functions from the ground up or scaling teams and tooling across an organization.
Demonstrated impact on revenue, retention, cost optimization, or product engagement through analytics-driven initiatives.