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Key Responsibilities and Required Skills for Analytics Developer

💰 $ - $

🎯 Role Definition

The Analytics Developer is a technically skilled, business-oriented professional responsible for designing, implementing, and maintaining analytics solutions that turn raw data into actionable business insights. This role combines advanced SQL and programming capabilities with strong data modeling, ETL, BI visualization, and stakeholder collaboration skills. Ideal candidates have experience building production-grade data pipelines, developing dashboards and reports, optimizing query performance, and embedding analytics into product workflows. Core keywords: Analytics Developer, data analytics, SQL, Python, ETL, BI, Tableau, Power BI, Looker, data modeling, Snowflake, BigQuery.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Data Analyst with solid SQL and dashboarding experience.
  • BI Developer or Report Developer transitioning to end-to-end analytics solutions.
  • Software Engineer or ETL Developer with interest in analytics and data modeling.

Advancement To:

  • Senior Analytics Developer / Lead Analytics Engineer
  • Analytics Engineering Manager or Data Engineering Manager
  • Product Analytics Lead or Data Science Engineer

Lateral Moves:

  • Business Intelligence Architect
  • Analytics Product Manager

Core Responsibilities

Primary Functions

  • Design, develop, and maintain scalable, production-quality ETL/ELT pipelines using SQL, Python, dbt, or other orchestration tools to ingest, transform, and curate large volumes of structured and semi-structured data for analytics and reporting.
  • Author, optimize, and maintain complex SQL queries and stored procedures for reporting, data validation, and performance-sensitive transformations within data warehouses such as Snowflake, BigQuery, Redshift, or Azure Synapse.
  • Build, deploy, and iterate interactive dashboards and operational reports using BI tools (Tableau, Power BI, Looker, Qlik) that answer business questions, support KPIs, and drive decisions across marketing, finance, product, and operations.
  • Translate business requirements into technical specifications and data models, collaborating with stakeholders to define metrics, dimensions, and SLAs for trusted metrics across the organization.
  • Implement dimensional data models (star/snowflake schemas), canonical data layer definitions, and semantic layers to ensure consistency of reporting and enable self-service analytics.
  • Author and maintain data documentation, data dictionaries, and semantic layer definitions to promote discoverability, reproducibility, and governance of analytical artifacts.
  • Instrument product and application events, design event schemas, and collaborate with engineering teams to ensure high-quality event tracking for accurate behavioral analytics and funnel/attribution analysis.
  • Build and maintain automated data quality frameworks, unit tests, anomaly detection, and monitoring that alert teams to pipeline failures, schema changes, or data drift.
  • Collaborate with Data Science teams to productionize ML features and predictions, integrating model outputs into dashboards, product experiences, and operational workflows.
  • Implement incremental and partitioned processing strategies, fine-tune pipeline performance, and manage cost-efficient data processing in cloud environments.
  • Perform root-cause analysis of data discrepancies, investigate incidents, and coordinate cross-functional remediation with engineering, product, and business stakeholders.
  • Design and execute A/B testing measurement frameworks, define experiment metrics, and deliver actionable experiment analysis to product managers and marketing teams.
  • Create reusable ETL components, macros, and templates to accelerate analytics development while ensuring consistency and maintainability across projects.
  • Enforce security, compliance, and access controls around sensitive datasets including PII/PHI, partnering with InfoSec and data governance teams to implement role-based access and masking where required.
  • Integrate third-party data sources (CRM, ad platforms, payment processors) via APIs and connectors to enrich analytics datasets and enable multi-channel attribution and ROI measurement.
  • Develop KPIs and executive-level dashboards that communicate performance trends, forecasts, and business impact in clear, actionable terms for senior leadership.
  • Collaborate in agile teams, participate in sprint planning, and estimate analytics work while balancing rapid iteration with data quality and technical debt reduction.
  • Migrate legacy reporting solutions to modern cloud-based analytics stacks and refactor monolithic ETL jobs into modular, testable pipelines.
  • Partner with business stakeholders to scope analytics projects, prioritize initiatives based on impact and effort, and deliver high-quality outcomes on schedule.
  • Conduct capacity planning and lifecycle management for data assets, archiving or purging stale datasets and optimizing storage and compute costs.
  • Create training materials and run workshops to upskill analysts and product teams in analytics tools, self-service reporting, and best practices for data-driven decision making.
  • Lead cross-functional data initiatives (e.g., master data alignment, attribution model redesign) and act as the analytics representative in business reviews and product planning sessions.
  • Evaluate and recommend new analytics technologies, tools, and frameworks that improve developer productivity, data reliability, and time-to-insight.

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)

  • Advanced SQL: complex joins, window functions, CTEs, query optimization, performance tuning.
  • Python (pandas, numpy) or R for data transformation, analytics automation, and scripting tasks.
  • Modern data warehouse experience: Snowflake, Google BigQuery, AWS Redshift, or Azure Synapse.
  • ETL/ELT tooling: dbt, Airflow, Fivetran, Stitch, Matillion, or custom orchestrations.
  • BI and visualization tools: Tableau, Power BI, Looker (LookML), Qlik — dashboard design and storytelling.
  • Data modeling: dimensional modeling, normalization, star schema design, and semantic layer creation.
  • Instrumentation and event tracking: understanding of analytics event schemas, Mixpanel/Segment/GA4.
  • Data quality and testing: unit tests for data pipelines, Great Expectations, data observability concepts.
  • Cloud platforms and infrastructure: AWS/GCP/Azure fundamentals, S3/GCS, IAM, networking basics.
  • SQL-based analytics performance tuning and cost optimization strategies for cloud warehouses.
  • Familiarity with data governance, privacy, and security practices (PII handling, role-based access).
  • Version control and CI/CD: Git, GitHub/GitLab workflows, automated deployment of analytics code.
  • Basic familiarity with Spark, Scala, or distributed processing for large-scale data transformations.
  • API integration and ELT of third-party systems (RESTful APIs, webhooks, SDKs).
  • Knowledge of experiment design and statistical analysis for A/B testing and causal inference.
  • Familiarity with containerization and lightweight orchestration: Docker, Kubernetes basics (preferred).
  • Ability to productionize machine learning features and collaborate with ML engineers on model deployment.

Soft Skills

  • Strong stakeholder management and the ability to translate business problems into analytical requirements.
  • Excellent written and verbal communication; able to present complex findings to non-technical audiences.
  • Critical thinking and structured problem solving with attention to detail and data accuracy.
  • Time management and prioritization skills in a fast-paced, cross-functional environment.
  • Collaborative mindset, comfortable working across analytics, engineering, product, and business teams.
  • Curiosity and continuous learning mindset to evaluate and adopt new analytics best practices and tools.
  • Ownership mentality with experience driving projects end-to-end from discovery to production.
  • Coaching and mentoring ability to uplift junior analysts and share best practices.
  • Adaptability to changing requirements and pragmatic decision-making under uncertainty.
  • Strong documentation practices and commitment to maintainable, testable analytics code.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Information Systems, Economics, or a closely related field; or equivalent practical experience.

Preferred Education:

  • Master’s degree in Data Science, Analytics, Computer Science, Business Analytics, or applied statistics.
  • Certifications in cloud platforms (e.g., Snowflake, Google Cloud Professional Data Engineer, AWS Data Analytics) or BI tools (Tableau, Power BI).

Relevant Fields of Study:

  • Computer Science / Software Engineering
  • Data Science / Applied Statistics
  • Mathematics / Applied Mathematics
  • Information Systems / Business Analytics
  • Economics / Operations Research

Experience Requirements

Typical Experience Range:

  • 3–6+ years of professional experience in analytics, BI, data engineering, or related roles. Junior Analytics Developer roles may start at 1–3 years; Senior roles typically require 5+ years.

Preferred:

  • Demonstrated experience building end-to-end analytics pipelines and dashboards in a cloud data stack.
  • Proven track record of delivering analytics projects that measurably improved business KPIs.
  • Experience working in Agile teams and collaborating with cross-functional partners in product, marketing, finance, and engineering.
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