Torchora
Back to Home

Key Responsibilities and Required Skills for Analytics Specialist

💰 $60,000 - $110,000

AnalyticsBusiness IntelligenceData

🎯 Role Definition

The Analytics Specialist is responsible for turning raw data into actionable insight that informs strategic decisions across the business. This role owns end-to-end analytics workflows: data extraction, transformation, modeling, visualization, reporting, and cross-functional delivery. An ideal candidate blends technical fluency in SQL, BI tooling, and scripting with business acumen and stakeholder management skills to prioritize analytics that drive measurable outcomes such as revenue growth, retention improvement, cost reduction, or operational efficiency.

Core goals:

  • Deliver accurate, timely dashboards and reports that align to KPIs and business goals.
  • Partner with product, marketing, finance and operations to translate business questions into data-driven solutions.
  • Build repeatable, scalable data processes and documentation to support self-serve analytics.

Keywords: Analytics Specialist, data analyst, business intelligence, SQL, Python, Tableau, Power BI, data visualization, KPI, A/B testing, ETL, stakeholder management.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Data Analyst / Data Analyst
  • Business Analyst with heavy metrics focus
  • Reporting Analyst or Operations Analyst

Advancement To:

  • Senior Analytics Specialist / Senior Data Analyst
  • Analytics Manager or BI Manager
  • Data Scientist or Revenue/Marketing Analytics Lead

Lateral Moves:

  • Product Analyst
  • Growth Analyst
  • Marketing Analytics Specialist

Core Responsibilities

Primary Functions

  • Lead the end-to-end development and maintenance of company-level dashboards and operational reports using SQL and BI tools (e.g., Tableau, Power BI, Looker), ensuring accuracy, consistency and actionable insights for leadership and cross-functional teams.
  • Translate complex business questions from stakeholders into analytic plans, selecting the appropriate data sources, metrics, and statistical methods to answer those questions and recommend next steps.
  • Write, optimize and document production-grade SQL queries and views to extract and transform data from transactional systems and data warehouses (e.g., Snowflake, BigQuery), with an emphasis on performance, reproducibility and data governance.
  • Design and implement measurement plans for product features and marketing campaigns including event tracking schema, KPI definitions, and attribution models to ensure consistent, reliable analysis over time.
  • Build and maintain scalable ETL/ELT pipelines or work with data engineering to ensure data availability, quality and lineage for analytic consumption, including monitoring jobs and triaging data issues.
  • Develop and execute A/B tests and experimentation frameworks: define hypotheses, slice cohorts, run significance testing, and produce clear experiment reports that inform product and marketing decisions.
  • Conduct deep-dive analyses to identify root causes of KPI changes (e.g., conversion funnel drops, churn increases), present findings with evidence, and propose prioritized recommendations that are tied to business impact.
  • Implement and maintain automated scheduled reports and alerting systems that proactively notify stakeholders about KPI anomalies or threshold breaches.
  • Create consolidated executive-level summary reports and narrative-driven dashboards that surface top trends, risks and opportunities for senior leadership review.
  • Perform statistical analysis and modeling (regression, time series, forecasting) to predict future trends and quantify the potential impact of strategic initiatives.
  • Collaborate with Product Managers and Engineers to instrument analytics events and ensure measurement is accurate, consistent and aligned with product roadmaps.
  • Design data models and semantic layers that standardize metric definitions across the organization to reduce ambiguity and rework in reporting.
  • Own data validation and QA processes: implement reconciliations between source systems and reports, document known limitations, and maintain an auditable trail of changes and assumptions.
  • Partner with marketing and growth teams to analyze channel performance, lifetime value (LTV), cohort retention, CAC and ROAS to drive budget allocation and optimization.
  • Build and maintain customer segmentation and behavioral cohorts to support personalization, targeting, and cross-sell/up-sell strategies.
  • Generate periodic performance reviews for business units (weekly, monthly, quarterly), highlighting trends, hypotheses, and recommended actions with quantified business impact.
  • Mentor junior analysts and help establish analytics best practices, code review processes, and documentation standards across the analytics function.
  • Evaluate and recommend analytics and visualization tools, data platform features and process improvements that increase speed-to-insight and reduce technical debt.
  • Ensure compliance with data privacy and security policies (e.g., GDPR, CCPA) when designing datasets and reports that include PII or sensitive information.
  • Engage in cross-functional planning to align analytics priorities with business objectives and product roadmaps, influencing near-term and long-term strategy based on data.
  • Maintain current knowledge of analytics trends, measurement frameworks, and industry benchmarks to apply best practices and refine internal analytics methodologies.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis to answer immediate stakeholder questions with high-quality, reproducible results.
  • Contribute to the organization's data strategy and roadmap by identifying analytics gaps, proposing tooling upgrades, and prioritizing enablement projects.
  • Collaborate with business units to translate data needs into engineering requirements and ensure instrumentation meets analytical needs.
  • Participate in sprint planning and agile ceremonies within the data engineering or product analytics teams; provide estimates and scope for analytics work.
  • Provide training and documentation for self-serve BI capabilities, enabling non-technical users to find and interpret data correctly.
  • Audit dashboards and reports regularly to retire outdated metrics and ensure only relevant, actionable visualizations remain in BI portals.
  • Facilitate cross-functional workshops to align stakeholders on common metric definitions, reporting cadence, and decision criteria.
  • Troubleshoot production incidents affecting reporting or data availability; coordinate with IT and engineering to restore service and implement preventive measures.
  • Partner with finance and operations to reconcile financial reports and operational metrics, supporting budgeting and forecasting cycles.
  • Drive continuous improvement by conducting post-mortem reviews for major experiments or reporting issues and institutionalizing learnings.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL: writing complex queries, window functions, performance tuning, and creating views/materialized tables for analytics.
  • BI & Dashboarding: hands-on experience with Tableau, Power BI, Looker, Mode, or equivalent visualization platforms to create executive and operational dashboards.
  • Data Warehousing & Cloud: practical experience with Snowflake, BigQuery, Redshift, or similar cloud data warehouses, including schema design and optimization.
  • Scripting & Automation: proficiency in Python or R for data manipulation, statistical analysis, ETL tasks and automation of recurring reports.
  • Event Tracking & Analytics Tools: working knowledge of analytics tagging and tools such as Google Analytics (GA4), Mixpanel, Amplitude, Segment for behavioral tracking and attribution.
  • ETL/ELT Tools and Frameworks: experience with dbt, Airflow, Fivetran, Stitch or similar tools to build and maintain reliable data pipelines.
  • Statistical Methods & Experimentation: solid understanding of A/B testing, hypothesis testing, regression analysis, and causal inference basics.
  • Data Modeling & Metric Governance: ability to build semantic layers, define canonical metrics, and maintain a metrics catalog to ensure consistent reporting.
  • SQL-driven Data Quality & Validation: implementing reconciliation scripts, anomaly detection, and monitoring dashboards for data integrity.
  • Cloud Platforms & SQL Interfaces: familiarity with AWS/GCP/Azure console basics and services that support analytics (e.g., S3, BigQuery, Redshift).
  • Reporting Automation & APIs: experience automating exports, integrating BI outputs with Slack/Email/Google Sheets and building API-driven reporting.
  • Familiarity with Machine Learning Concepts: awareness of basic ML pipelines and when to leverage models for forecasting, propensity scoring or personalization.

Soft Skills

  • Strong business acumen with the ability to translate ambiguous business problems into measurable analytics deliverables.
  • Excellent stakeholder management: prioritize requests, set expectations, and communicate results and limitations clearly to technical and non-technical audiences.
  • Data storytelling: synthesize complex analyses into concise narratives and visualizations that drive decisions.
  • Critical thinking and problem-solving: approach root cause analysis systematically and recommend practical, evidence-based solutions.
  • Attention to detail and commitment to data accuracy and reproducibility.
  • Project management and organizational skills: manage multiple analytics projects and deliverables on schedule.
  • Collaborative mindset: work effectively across product, engineering, marketing, finance and operations teams.
  • Adaptability and continuous learning: quickly adopt new tools, methodologies and changing business needs.
  • Mentorship and knowledge sharing: train colleagues on analytics best practices and encourage a culture of data literacy.
  • Ethical judgment: handle sensitive data responsibly and make decisions mindful of privacy and compliance constraints.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master’s degree in Data Science, Analytics, Business Analytics, Applied Statistics, or MBA with strong analytics concentration.

Relevant Fields of Study:

  • Data Science / Data Analytics
  • Statistics / Applied Mathematics
  • Computer Science / Information Systems
  • Economics / Business / Finance
  • Marketing Analytics / Operations Research

Experience Requirements

Typical Experience Range: 2 - 6 years in analytics, business intelligence, or data-focused roles with demonstrated ownership of dashboards, reporting, and stakeholder-facing projects.

Preferred:

  • 3+ years in product, marketing, revenue or operations analytics at a fast-paced environment or tech company.
  • Proven track record of delivering measurable business impact from analytical work, experience implementing A/B testing and building production-grade SQL-driven analytics pipelines.
Explore More Careers