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Key Responsibilities and Required Skills for Data Analytics Analyst

💰 $70,000 - $110,000

Data AnalyticsBusiness IntelligenceAnalyticsBI

🎯 Role Definition

The Data Analytics Analyst transforms raw data into actionable insights that drive business decisions. This role blends strong technical skills (SQL, Python/R, ETL, BI tools) with business acumen and stakeholder communication to design reports, build dashboards, perform advanced analyses, measure product and marketing performance, and support strategic planning initiatives. The Data Analytics Analyst partners with product, marketing, finance, and engineering teams to define metrics, maintain data quality, and deliver repeatable analytics solutions that scale.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Data Analyst
  • Business Analyst or Marketing Analyst
  • BI/Reporting Specialist

Advancement To:

  • Senior Data Analyst / Lead Analyst
  • Analytics Manager / BI Manager
  • Data Scientist or Product Analytics Lead

Lateral Moves:

  • Business Intelligence Developer
  • Data Engineer
  • Product Manager (analytics-focused)

Core Responsibilities

Primary Functions

  • Own end-to-end analytics deliverables: design, implement, and maintain dashboards, scheduled reports, and self-service analytics for product, marketing, finance, and operations stakeholders using tools like Tableau, Power BI, or Looker.
  • Write, optimize, and maintain complex SQL queries and stored procedures to extract, transform, and aggregate data from relational and analytical databases (Redshift, Snowflake, BigQuery) for reporting and analysis.
  • Perform data discovery and profiling to identify anomalies, gaps, and opportunities; implement data validation rules and work with engineering to remediate data quality issues.
  • Build and maintain repeatable ETL/ELT pipelines, leveraging tools such as dbt, Airflow, or proprietary workflows to ensure reliable data delivery and lineage.
  • Translate ambiguous business questions into clear, testable analytics questions and hypotheses, then deliver actionable insights and recommended next steps for stakeholders.
  • Conduct statistical analyses including A/B testing, cohort analysis, regressions, and forecasting to evaluate product changes, marketing campaigns, and revenue drivers.
  • Design and document business metrics and KPI definitions, maintain a metrics catalog, and enforce consistency across dashboards and reports to prevent metric drift.
  • Create segmentation analyses (customer lifetime value, churn risk, usage patterns) and build scoring models to prioritize retention and acquisition strategies.
  • Implement instrumentation requirements for product and event tracking (e.g., adjust analytics events, naming conventions, tracking plans) and validate event quality in staging and production.
  • Partner with product managers and engineers to translate analytics outputs into product requirements and to validate release impact against hypotheses.
  • Automate recurring analyses and reporting tasks through scripting (Python/R), scheduling, and templated dashboards to increase analyst productivity and reduce manual work.
  • Provide ad-hoc, high-impact analyses for leadership including scenario modeling, what-if analysis, and ROI calculations to support strategic decisions and board-level reports.
  • Monitor and troubleshoot data pipeline failures, proactively escalate issues, and coordinate cross-functional fixes to minimize downtime for business-critical reporting.
  • Develop and execute data-driven playbooks and dashboards for monitoring acquisition, activation, retention, revenue (AARRR) metrics across channels and cohorts.
  • Create clear visualizations and narratives tailored to audience level — from executives (summary KPIs and one-page insights) to technical teams (data dictionaries and query artifacts).
  • Perform competitor and market analytics using third-party data and web analytics (Google Analytics, Adobe Analytics) to inform positioning and channel strategies.
  • Contribute to build-out of BI architecture, advise on tool selection, and collaborate with data engineering on data modeling best practices (star/snowflake schemas, dimensional modeling).
  • Maintain documentation for queries, models, data definitions, and analytical methods to ensure reproducibility and knowledge transfer.
  • Mentor junior analysts, lead code and dashboard reviews, and drive best practices for analytics, version control (Git), and testing.
  • Ensure compliance with data governance, privacy (GDPR/CCPA as applicable), and security policies when handling PII and sensitive datasets.
  • Partner with finance and operations to reconcile reported metrics, explain variances, and support budgeting and forecasting processes.
  • Evaluate and integrate new data sources (third-party APIs, CRM exports, ad platforms) to enrich analysis pipelines and enable more comprehensive insights.

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.
  • Deliver training and office hours for business users to increase adoption of self-service analytics tools.
  • Lead small cross-functional analytics projects from scoping through deployment and post-launch measurement.
  • Help define instrumentation and logging standards to improve downstream analytics reliability.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL for analytics: window functions, CTEs, partitioning, query optimization.
  • Proficient in Python or R for data manipulation, statistical analysis, and automation (pandas, numpy, scipy, tidyverse).
  • Experience with BI and visualization tools: Tableau, Power BI, Looker, or Qlik.
  • Familiarity with cloud data warehouses: Snowflake, BigQuery, Amazon Redshift or Azure Synapse.
  • ETL/ELT pipeline experience with tools like dbt, Airflow, Fivetran, Stitch or custom scripts.
  • Strong understanding of data modeling concepts: star schemas, fact/dimension tables, normalized vs denormalized models.
  • Working knowledge of A/B testing frameworks, statistical significance, and experiment design.
  • Data wrangling and profiling skills; ability to write robust data validation and QA checks.
  • Experience with web and product analytics: Google Analytics, Segment, Amplitude, Mixpanel.
  • Version control and collaborative workflows: Git, code review processes.
  • Familiarity with SQL-based performance tuning and cost-aware querying in cloud environments.
  • Basic knowledge of machine learning concepts and common libraries (scikit-learn, statsmodels) for feature engineering and predictive scoring.
  • Experience with business systems and data sources: CRM (Salesforce), ad platforms (Google Ads, Meta), ERP/finance systems.

Soft Skills

  • Strong business acumen and ability to translate data insights into concrete business recommendations.
  • Clear, concise communication and storytelling skills for technical and non-technical audiences.
  • Stakeholder management: prioritize requests, set expectations, and deliver timely insights.
  • Analytical problem solving with intellectual curiosity and attention to detail.
  • Project management: able to scope, estimate, and deliver analytics projects on time.
  • Collaboration and cross-functional influence across product, engineering, marketing, and finance teams.
  • Adaptability to shifting priorities in fast-paced environments.
  • Teaching and mentoring ability to raise the analytics maturity of the organization.
  • Ethical judgment when handling sensitive or personal data.
  • Time management and ability to balance long-term projects with urgent ad-hoc requests.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master's degree in Data Science, Analytics, Business Analytics, Statistics, or related field; or equivalent professional certifications (e.g., Tableau Specialist, Google Data Analytics Certificate).

Relevant Fields of Study:

  • Computer Science
  • Statistics or Applied Mathematics
  • Economics
  • Data Science / Analytics
  • Information Systems
  • Business Administration with quantitative emphasis
  • Engineering

Experience Requirements

Typical Experience Range:

  • 2–5 years working in data analytics, business intelligence, or a related analytics role.

Preferred:

  • 3+ years experience in product, marketing, or finance analytics with demonstrated impact.
  • Proven track record building production dashboards, automating reporting, and running experiments.
  • Experience operating within cloud data stack and modern BI tooling; demonstrated ability to work cross-functionally and influence product decisions.
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