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Key Responsibilities and Required Skills for Data Insights Analyst

💰 $70,000 - $110,000

Data AnalyticsBusiness IntelligenceData Science

🎯 Role Definition

The Data Insights Analyst is a business-facing analytics professional who translates raw data into clear, actionable insights that inform strategy, product decisions, marketing activities, and operational improvements. This role combines strong SQL and data visualization skills with statistical thinking, domain knowledge, and stakeholder management to define KPIs, deliver repeatable reporting, run ad-hoc and exploratory analyses, and embed analytics into decision-making processes. Ideal candidates are fluent in BI tools (Tableau, Power BI, Looker), comfortable working with modern data warehouses (Snowflake, BigQuery, Redshift), and experienced in shaping data requirements and delivering high-impact insights for cross-functional teams.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Data Analyst
  • Business Analyst with analytics exposure
  • Marketing or Product Analyst with SQL/BI experience

Advancement To:

  • Senior Data Insights Analyst / Senior Business Intelligence Analyst
  • Analytics Manager / BI Manager
  • Product Analytics Lead / Data Science Manager

Lateral Moves:

  • Data Engineer (with additional engineering training)
  • Product Manager (product analytics specialization)
  • Growth or Marketing Analytics Lead

Core Responsibilities

Primary Functions

  • Design, develop, and maintain interactive dashboards and executive reports using Tableau, Power BI, or Looker to communicate performance trends, KPIs, and business insights to stakeholders across product, marketing, finance, and operations.
  • Write, optimize, and maintain complex SQL queries and stored procedures to extract, transform and aggregate data from relational and columnar data warehouses (Snowflake, BigQuery, Redshift) for reporting and analysis.
  • Define, measure, and monitor key performance indicators (KPIs) and metrics frameworks that reflect business objectives, ensuring metric definitions are documented, consistent, and trusted across teams.
  • Lead and execute ad-hoc analyses to answer high-impact business questions (e.g., customer segmentation, churn drivers, funnel conversion performance), synthesize findings, and make clear recommendations to business leaders.
  • Design and analyze A/B tests and experiments end-to-end, including hypothesis formulation, sample sizing, metric selection, statistical testing, and communicating results and business impact.
  • Partner closely with product managers, marketing, finance, and operations to translate business questions into analytical requirements, analysis plans, and prioritized dashboards or reports.
  • Collaborate with data engineering to specify data models, ETL/ELT jobs, and transformation requirements to ensure accurate, timely, and scalable data availability for downstream analytics.
  • Build and maintain automated reporting pipelines and scheduled refresh processes so stakeholders have up-to-date operational and strategic reports without manual intervention.
  • Conduct cohort analyses and customer lifetime value modeling to inform retention strategies, acquisition ROI, and pricing decisions.
  • Evaluate data quality, identify upstream data integrity issues, and drive remediation efforts with engineering and source system owners to ensure analytic reliability.
  • Create reproducible analysis artifacts (notebooks, SQL scripts, BI dashboards) with clear documentation and version control so results can be audited and reused by colleagues.
  • Translate complex quantitative results into compelling narratives and visualizations tailored for executive leadership, non-technical stakeholders, and cross-functional teams.
  • Perform segmentation and propensity modeling using statistical and ML-lite techniques (logistic regression, decision trees, clustering) to surface high-value customer segments and personalization opportunities.
  • Monitor product and marketing funnel metrics, identify drop-off points, and recommend actionable experiments and product changes to improve conversion and engagement.
  • Establish and maintain a centralized metric catalog or data dictionary to ensure consistent metric usage and reduce ambiguity across reports and analyses.
  • Support pricing analysis and revenue forecasting by synthesizing transactional, behavioral, and market data to model revenue scenarios and sensitivity analyses.
  • Provide mentorship and analytic coaching to less-experienced analysts, helping to raise the overall analytics maturity and adoption of best practices.
  • Keep abreast of industry trends, new BI and analytics tools, and modern data stack capabilities to continually improve analytics workflows and tooling.
  • Lead cross-functional analytics projects—scoping deliverables, aligning stakeholders, setting milestones, and ensuring timely and high-quality outcomes.
  • Conduct root cause analyses for operational issues, incidents, and dips in business performance; provide clear remediation plans and monitor outcomes.
  • Implement and maintain tracking instrumentation quality by working with product and engineering teams on analytics event taxonomy and client-side/server-side logging.
  • Produce executive-ready slide decks and briefing notes summarizing analytical insights, actions taken, and estimated impact to support strategic decision-making.

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.

Required Skills & Competencies

Hard Skills (Technical)

  • Expert SQL skills (complex joins, window functions, CTEs, performance tuning) for data extraction and transformation.
  • Proficiency with BI and dashboarding tools: Tableau, Power BI, Looker (LookML) or equivalent.
  • Experience with modern cloud data warehouses: Snowflake, Google BigQuery, AWS Redshift, or similar platforms.
  • Strong analytical programming in Python (pandas, numpy) and/or R for data manipulation, statistical analysis, and automation.
  • Familiarity with ETL/ELT tooling and transformation frameworks: dbt, Airflow, Fivetran or Stitch.
  • Solid understanding of statistics and experimental design (A/B testing, hypothesis testing, confidence intervals, power analysis).
  • Knowledge of data modeling concepts (star schema, dimensional modeling) and schema design for analytics.
  • Experience with analytics and tracking stacks: Google Analytics / GA4, Segment / RudderStack, Mixpanel, Amplitude.
  • Basic exposure to machine learning concepts and libraries (scikit-learn) for propensity scoring and predictive analytics.
  • Advanced Excel skills including pivot tables, Power Query, and complex formulae for rapid, ad-hoc analysis.
  • Familiarity with version control (Git) and reproducible analysis practices (notebooks, scripts).
  • Experience with data governance practices, metric catalogs, and ensuring data lineage and documentation.

Soft Skills

  • Strong business acumen with the ability to connect data insights to strategic decisions and commercial impact.
  • Outstanding written and verbal communication; able to craft concise narratives and present technical findings to non-technical audiences.
  • Stakeholder management: ability to gather requirements, set expectations, and deliver analyses that solve real business problems.
  • Critical thinking and problem solving with a hypothesis-driven approach to analysis.
  • Curiosity and intellectual rigor—comfortable asking penetrating questions and validating assumptions in the data.
  • Attention to detail and a commitment to data accuracy and reproducibility.
  • Time management and prioritization skills to balance recurring reporting, ad-hoc requests, and project work.
  • Collaboration and teamwork—works effectively with product, engineering, marketing, finance, and operations.
  • Adaptability to evolving data environments and changing business priorities.
  • Coaching and mentorship—willingness to upskill team members and share best practices.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in a quantitative or business discipline such as Statistics, Mathematics, Computer Science, Economics, Data Science, Business Analytics, or Information Systems.

Preferred Education:

  • Master's degree in Data Science, Business Analytics, Statistics, Applied Mathematics, Economics, or an MBA with strong analytics focus.

Relevant Fields of Study:

  • Data Science
  • Statistics
  • Economics
  • Computer Science
  • Mathematics
  • Business Analytics
  • Information Systems

Experience Requirements

Typical Experience Range:

  • 2–5 years of hands-on analytics experience in a business intelligence, product analytics, marketing analytics, or similar role.

Preferred:

  • 3–7+ years of progressively responsible experience delivering analytics and BI solutions in a product-led or data-driven organization.
  • Demonstrated track record of designing dashboards, running experiments, and driving measurable business outcomes.
  • Prior experience working with cloud data warehouses (Snowflake/BigQuery/Redshift), BI platforms (Tableau/Power BI/Looker), and modern ETL/ELT tooling (dbt, Airflow).
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