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Key Responsibilities and Required Skills for Data Analytics Specialist

💰 $70,000 - $120,000

Data AnalyticsBusiness IntelligenceAnalyticsData ScienceReporting

🎯 Role Definition

The Data Analytics Specialist is responsible for transforming raw data into actionable insights that support decision-making across product, marketing, finance, and operations. This role blends hands-on data wrangling, KPI definition, dashboard development, statistical analysis, and cross-functional stakeholder engagement to drive measurable business outcomes. The ideal candidate is fluent in SQL, modern BI tools (Power BI / Tableau / Looker), basic scripting (Python or R), and has experience operationalizing analytics in production environments.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst (individual contributor focused on ad-hoc analysis and reporting)
  • Business Analyst (domain-oriented analyst converting business requirements into metrics)
  • Reporting Analyst / BI Developer (focused on dashboards and ETL pipelines)

Advancement To:

  • Senior Data Analyst / Lead Analytics Specialist
  • Analytics Manager / BI Manager
  • Data Scientist or Machine Learning Engineer (with additional modeling focus)

Lateral Moves:

  • Product Analyst / Growth Analyst
  • Data Engineer (with emphasis on pipelines and engineering)
  • Business Intelligence Developer

Core Responsibilities

Primary Functions

  • Design, build, and maintain end-to-end dashboards and executive-level reports using Power BI, Tableau, or Looker that clearly communicate trends, KPIs, and business performance to stakeholders across marketing, product, finance, and operations.
  • Write efficient, maintainable SQL queries to extract, aggregate, and transform large datasets from relational and cloud warehouses (Snowflake, BigQuery, Redshift) to support daily, weekly, and ad-hoc analyses.
  • Develop and maintain automated ETL/ELT processes using tools like dbt, Airflow, Matillion, or native cloud pipelines to ensure timely and reliable data availability for analytics.
  • Define, document, and operationalize key performance indicators (KPIs) and business metrics, ensuring consistent definitions across systems and teams and driving alignment on measurement.
  • Perform exploratory and confirmatory statistical analyses (cohort analysis, regression, hypothesis testing) to identify drivers of business performance and quantify impact of product and marketing experiments.
  • Partner with product, marketing, sales, and finance stakeholders to translate business questions into analytics specifications, deliverables, and prioritized work items.
  • Create reproducible analysis and reporting artifacts (notebooks, SQL scripts, BI dashboards) and maintain version control using Git or enterprise repositories to support transparency and collaboration.
  • Implement and monitor data quality checks, validation rules, and anomaly detection to ensure analytic outputs are accurate and trusted by the business.
  • Conduct A/B test design, analysis, and interpretation—calculating sample sizes, running significance tests, and providing clear recommendations based on experiment outcomes.
  • Build forecasting models and trend analyses (time-series, seasonality adjustments) to support demand planning, revenue forecasting, and budgeting processes.
  • Translate analytic findings into concise, compelling storytelling with visualizations and slide decks tailored for executive briefings and cross-functional teams.
  • Optimize query performance and storage costs through indexing, partitioning, and refactoring heavy analytics workloads in cloud data warehouses.
  • Collaborate with data engineers to design and iterate on data models, star schemas, and semantic layers that accelerate self-service analytics across the organization.
  • Create and maintain metadata, data dictionaries, and lineage documentation to improve data discoverability and governance.
  • Drive ad-hoc deep-dive analyses to investigate outliers, operational incidents, and strategic opportunities, delivering clear hypotheses, methods, and actionable recommendations.
  • Build customer segmentation and lifetime value (LTV) models to inform acquisition, retention, and pricing strategies.
  • Implement instrumentation and event tracking best practices for analytics platforms (e.g., GTM, Segment, Snowplow) to ensure events are captured consistently and reliably for downstream analysis.
  • Educate and enable business users on self-service BI capabilities, providing training sessions, office hours, and documented playbooks for common analytics workflows.
  • Collaborate with legal and compliance teams to ensure analytics implementations comply with privacy regulations (GDPR, CCPA) and internal data access policies.
  • Monitor and report on operational KPIs (SLAs, system health metrics) for analytics pipelines and BI platforms, escalating and remediating issues when necessary.
  • Lead cross-functional analytics projects end-to-end: gather requirements, define success metrics, conduct analysis, implement reporting, and measure impact post-deployment.
  • Maintain cost and performance visibility for analytics infrastructure and recommend improvements to reduce cloud spend while improving query responsiveness.
  • Support the design and rollout of tagging conventions, naming standards, and governance frameworks to standardize analytics across multiple teams and products.
  • Evaluate new analytics tools and technologies, run proof-of-concepts, and recommend vendor or open-source solutions that improve efficiency, scalability, or insight quality.

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.
  • Assist in vendor evaluations and procurement for BI and analytics solutions.
  • Help maintain role-based access controls and data access audits to secure sensitive data.
  • Mentor junior analysts and contribute to hiring and onboarding processes for analytics hires.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL (window functions, CTEs, performance tuning) for querying OLAP/OLTP systems and cloud warehouses (Snowflake, BigQuery, Redshift).
  • BI and reporting tools: Power BI, Tableau, Looker, or equivalent — experience building dashboards, data models, and semantic layers.
  • Scripting for analysis and automation: Python (pandas, numpy, scikit-learn) or R for statistical analysis and ETL tasks.
  • Data modeling and warehousing concepts: star/snowflake schemas, dimensional modeling, and schema design for analytics.
  • Familiarity with dbt (data transformations), Airflow / Prefect (orchestration), and modern ELT tooling.
  • Experience with cloud data platforms and storage: AWS, GCP, Azure, Snowflake, BigQuery.
  • Statistical analysis and experimental design: hypothesis testing, A/B testing, regression analysis, and time-series forecasting.
  • Basic knowledge of machine learning concepts and model evaluation metrics (classification, regression, clustering).
  • Proficiency with Excel for rapid analysis, pivot tables, and ad-hoc reporting.
  • Data quality, observability, and monitoring: automated tests, data contracts, anomaly detection.
  • Understanding of event tracking and analytics instrumentation (Segment, GTM, Snowplow, Amplitude).
  • Version control and collaborative development: Git, CI/CD practices for analytics code deployment.
  • API usage and data integration: REST/GraphQL basics for pulling/pushing data between systems.
  • Familiarity with data governance, privacy (GDPR/CCPA), and role-based access control best practices.
  • Experience with visualization best practices, accessibility, and storytelling to non-technical audiences.

Soft Skills

  • Strong verbal and written communication; able to present complex analyses to C-level and cross-functional audiences.
  • Stakeholder management: gather requirements, set expectations, and prioritize analytics deliverables.
  • Problem-solving and critical thinking: isolate root causes, design experiments, and propose pragmatic solutions.
  • Business acumen: translate metrics into strategic recommendations that drive measurable outcomes.
  • Time management and organization: manage multiple concurrent analytics projects with competing priorities.
  • Collaborative mindset: work effectively with product, engineering, finance, and operations teams.
  • Teaching and coaching: enable self-service analytics and mentor junior team members.
  • Attention to detail and ownership: produce reproducible, validated work and see projects through to impact.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master's degree in Data Science, Analytics, Statistics, Business Analytics, or an MBA with strong quantitative focus.
  • Relevant industry certifications (e.g., Tableau Certification, Microsoft Certified: Data Analyst Associate, Google Data Analytics Certificate, dbt Fundamentals).

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range: 3 - 6 years of hands-on analytics experience in roles such as Data Analyst, BI Analyst, or Analytics Specialist.

Preferred:

  • 4+ years working with cloud data warehouses and modern BI tools.
  • Demonstrated track record delivering measurable outcomes through analytics (e.g., increased retention, improved conversion, cost savings).
  • Experience working in cross-functional product, marketing, finance, or operations teams and translating business needs into technical solutions.
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