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Key Responsibilities and Required Skills for Data Analytics Manager

💰 $ - $

🎯 Role Definition

The Data Analytics Manager leads cross-functional analytics initiatives that turn raw data into actionable insights. You will design and execute analytics strategy, build and maintain scalable BI platforms and dashboards, manage a team of analysts and data engineers, and partner with business stakeholders to define KPIs, measure performance, and drive data-informed decisions. This role requires a strong blend of technical expertise (SQL, Python, ETL, cloud data warehouses), people leadership, and strategic business acumen to translate analytics into measurable business outcomes.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Senior Data Analyst with deep SQL, visualization and stakeholder-facing experience
  • Business Intelligence (BI) Lead who has owned dashboards and reporting
  • Analytics Consultant or Product Analytics Senior Specialist focused on growth metrics

Advancement To:

  • Director of Analytics / Head of Analytics
  • Senior Director, Insights & Data Science
  • Chief Data Officer (CDO) / VP of Data & Analytics

Lateral Moves:

  • Product Analytics Manager / Growth Analytics Manager
  • Data Engineering Manager
  • Business Intelligence Architect

Core Responsibilities

Primary Functions

  • Lead and grow a high-performing analytics team (data analysts, BI engineers, and data scientists), including recruiting, mentoring, performance management, and career development planning to ensure delivery of business-critical analytics and reporting.
  • Define and own the analytics strategy and roadmap, aligning analytics initiatives with company objectives and translating stakeholder priorities into measurable metrics, timelines, and deliverables.
  • Design, implement, and maintain enterprise-grade dashboards and self-service BI solutions (Tableau, Power BI, Looker) to enable business units to access trusted KPIs and make timely decisions.
  • Serve as the primary analytics partner to cross-functional stakeholders (product, marketing, finance, operations, sales), translating business questions into analytical requirements and shaping data-driven strategies.
  • Architect and oversee ETL/ELT processes and data pipelines (dbt, Fivetran, Airflow, Informatica), ensuring timely, accurate, and scalable data ingestion from multiple sources into cloud data warehouses (Snowflake, BigQuery, Redshift).
  • Establish and enforce data quality frameworks, monitoring, and alerting to ensure data reliability for operational reporting and strategic analytics, including root-cause analysis and remediation plans.
  • Define, standardize, and document core business metrics, data definitions, and KPI taxonomies to eliminate discrepancies and create a single source of truth across the organization.
  • Lead advanced analytics and experimentation efforts (A/B testing, causal inference), partnering with product and growth teams to design, analyze, and operationalize experiments that drive user engagement and revenue.
  • Oversee predictive and prescriptive modeling projects in collaboration with data science teams, ensuring models are production-ready, monitored for drift, and integrated into business workflows.
  • Prioritize analytics backlog and manage delivery using agile methodologies; work with product owners and engineering to scope, estimate, and deliver analytics features on time.
  • Present insights, trends, and actionable recommendations to senior leadership and executive stakeholders through clear storytelling, data visualization, and business impact quantification.
  • Manage vendor and tool relationships for analytics and BI (visualization, data integration, monitoring, feature stores), evaluating new technology and negotiating contracts to optimize cost and capability.
  • Build and operationalize performance measurement frameworks for key initiatives (marketing ROI, product funnels, lifecycle metrics), including dashboards, cohort analyses, and attribution models.
  • Implement monitoring and SLA processes for analytics deliverables, ensuring uptime, performance, and rapid response to data incidents or business-critical ad-hoc requests.
  • Drive data governance and compliance initiatives (GDPR, CCPA, SOC2), collaborating with legal and security teams to ensure analytics practices meet regulatory and privacy requirements.
  • Translate complex analytical results into clear, business-focused recommendations and action plans, enabling business owners to take measurable steps based on findings.
  • Champion self-service analytics by enabling business users with templates, training, governed data marts, and access controls to reduce dependency on centralized analytics for routine questions.
  • Optimize data architecture and BI performance by working with engineering and cloud teams to tune queries, partitioning strategies, and data model design for fast, cost-efficient reporting.
  • Create and maintain comprehensive analytics documentation, runbooks, and knowledge bases so insights and processes are reproducible and auditable.
  • Oversee budgeting and resource allocation for analytics projects, balancing near-term tactical needs with strategic investments in tooling, training, and automation.
  • Identify opportunities to automate recurring analyses and reporting through SQL templating, parameterized dashboards, or data productization to improve speed and reduce manual work.
  • Coordinate cross-functional initiatives such as revenue forecasting, churn prediction, and pricing analytics, delivering robust models and actionable plans to the business.
  • Foster a culture of data literacy across the organization by designing training programs, office hours, and stakeholder engagement to uplift analytical capability and adoption.

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.
  • Run recurring stakeholder syncs and analytics reviews to align priorities and surface high-impact insights.
  • Provide coaching and internal enablement sessions on analytics best practices, SQL, and dashboard design.
  • Evaluate and pilot new analytics tools and vendor solutions to keep the analytics stack modern and efficient.
  • Maintain data access controls and ensure secure sharing of analytics artifacts across teams.

Required Skills & Competencies

Hard Skills (Technical)

  • SQL — Advanced querying, complex joins, window functions, performance tuning and query optimization for large-scale data warehouses.
  • Data Warehousing & Cloud Platforms — Hands-on experience with Snowflake, BigQuery, Amazon Redshift or Azure Synapse and designing scalable data models (star/snowflake schemas).
  • ETL/ELT & Orchestration — Practical knowledge of dbt, Airflow, Fivetran, Stitch or similar tools for transforming and orchestrating data pipelines.
  • BI & Visualization — Expert-level use of Tableau, Power BI, Looker, or comparable BI tools to design executive dashboards and interactive analytics.
  • Programming for Analytics — Proficiency in Python or R for data manipulation, statistical analysis, and building reproducible analytics pipelines.
  • Data Modeling & Schemas — Ability to design logical and physical data models, dimensional modeling, and maintain a metric/semantic layer.
  • Statistical Analysis & Experimentation — Solid understanding of hypothesis testing, A/B testing, causal inference, and basic predictive modeling techniques.
  • Data Governance & Privacy — Experience implementing data governance frameworks, metadata management, lineage tracking, and privacy/compliance controls (GDPR/CCPA).
  • Monitoring & Observability — Implementing data quality checks, SLAs, alerting and instrumentation for analytics pipelines and dashboards.
  • Cloud & DevOps Familiarity — Familiar with cloud infrastructure (AWS/GCP/Azure), containerization basics, and CI/CD practices for analytics code deployment.
  • Advanced Excel & Spreadsheet Modeling — For quick analysis, modeling and stakeholder-ready financial or operational summaries.
  • API & Data Integration — Experience integrating third-party data sources, RESTful APIs, and streaming/real-time data when required.
  • Machine Learning Concepts — Familiarity with model lifecycle, feature engineering, model evaluation, and productionization considerations.
  • SQL Version Control & Collaboration — Using Git, code review practices, and modularized SQL/analytics codebases (e.g., dbt projects).
  • Reporting Automation — Building parameterized reports, scheduled extracts, and automated distribution of insights.

Soft Skills

  • Leadership & People Management — Proven ability to hire, mentor, and build a collaborative analytics team culture.
  • Stakeholder Management — Skilled at managing expectations, prioritizing requests, and influencing cross-functional leaders without direct authority.
  • Business Acumen — Strong commercial awareness to convert analytics into measurable business impact and to prioritize high-value initiatives.
  • Communication & Storytelling — Translate complex analysis into simple, persuasive narratives and clear visualizations for executives.
  • Strategic Thinking — Can balance short-term reporting needs with long-term platform and capability building.
  • Problem Solving — Structured thinker who breaks down ambiguous business problems into analytical solutions and experiments.
  • Prioritization & Time Management — Wealth of experience in managing competing deadlines and driving high-ROI analytics work.
  • Collaboration & Cross-functional Influence — Works effectively across product, engineering, marketing, finance and operations to deliver outcomes.
  • Change Management — Ability to lead adoption of new processes, tools, and data-driven ways of working across teams.
  • Attention to Detail & Quality Focus — Rigor in validating data, documentation, and analysis before business consumption.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master’s degree in Data Science, Business Analytics, Statistics, or an MBA with a strong analytics focus.

Relevant Fields of Study:

  • Data Science / Machine Learning
  • Statistics / Applied Mathematics
  • Computer Science / Software Engineering
  • Economics / Quantitative Finance
  • Business Analytics / Operations Research

Experience Requirements

Typical Experience Range:

  • 5–10 years of professional analytics experience with progressive responsibility; 3+ years managing analytics or BI teams preferred.

Preferred:

  • 8+ years in analytics roles with demonstrated leadership in building analytics platforms, delivering enterprise BI solutions, and partnering with senior stakeholders to drive measurable business outcomes. Familiarity with cloud data stacks (Snowflake/BigQuery/Redshift), modern ETL/ELT tooling (dbt, Airflow, Fivetran), and visualization suites (Tableau, Power BI, Looker) is highly desirable.
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