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Key Responsibilities and Required Skills for Insight Developer

💰 $ - $

🎯 Role Definition

The Insight Developer is a data-focused professional responsible for designing, building, and maintaining analytics solutions that turn raw data into actionable business insights. This role blends business analysis, data engineering, and BI/reporting expertise to deliver dashboards, reports, and analytical products that inform strategy, improve operational performance, and drive measurable outcomes across marketing, finance, product, and operations. The Insight Developer partners closely with stakeholders to define KPIs, architect data models, implement ETL/ELT processes, and ensure data quality, security, and governance.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst with strong SQL and dashboarding experience.
  • Business Intelligence (BI) Developer or Report Developer.
  • ETL Developer or Junior Data Engineer.

Advancement To:

  • Senior Insight Developer / Senior BI Developer.
  • Analytics Manager or BI Manager.
  • Data Engineering Lead or Head of Analytics.

Lateral Moves:

  • Data Engineer (focus on pipelines and platform).
  • Product Analyst or Business Analyst (domain-focused analytics).

Core Responsibilities

Primary Functions

  • Lead the end-to-end development of data visualizations and dashboards (Power BI, Tableau, Looker) that clearly communicate KPIs, trends, and root causes to business stakeholders and senior leadership.
  • Translate ambiguous business questions into concrete analytical requirements and technical specifications, partnering with product managers and business owners to prioritize analytics work.
  • Design, implement, and maintain robust ETL/ELT pipelines using tools and frameworks such as dbt, Airflow, Informatica, Azure Data Factory, or equivalent to ingest, transform, and curate data for reporting and analytics.
  • Develop and optimize complex SQL queries and data models to power high-performance reports and ad-hoc analysis; tune queries for cost and speed in cloud data warehouses (Snowflake, BigQuery, Redshift).
  • Build and maintain production-grade semantic models and data marts (dimensional/star schemas) that support consistent metrics, cross-functional reporting, and self-service analytics.
  • Implement data quality checks, anomaly detection, and monitoring dashboards to ensure accuracy, completeness, and reliability of analytics outputs and to proactively escalate data incidents.
  • Collaborate with data engineering and platform teams to define data ingestion patterns, schema evolution, partitioning strategies, and resource allocation for scalable analytics.
  • Create reusable, documented data transformations and analytics code (SQL, Python, R) following best practices for version control (Git), code review, and CI/CD deployments.
  • Partner with product, marketing, finance, and operations teams to define and operationalize business metrics, SLAs, and attribution models that align to company objectives and OKRs.
  • Conduct exploratory data analysis and statistical experiments (A/B tests, cohort analysis, regression) to surface actionable insights that drive product changes, marketing optimization, and revenue growth.
  • Implement role-based access, row-level security, and governance policies in BI tools and data platforms to protect sensitive data while enabling self-service access for authorized users.
  • Design and deliver scalable reporting solutions for recurring operational needs (executive dashboards, weekly scorecards, financial close reporting) with automation and alerting.
  • Lead performance tuning and cost optimization for cloud-based analytics workloads, including warehouse sizing, query optimization, caching strategies, and materialized views.
  • Translate complex analytical findings into concise, story-driven presentations and one-pagers targeted to non-technical stakeholders, including recommended actions and business impact estimates.
  • Mentor and onboard junior analysts and developers; create documentation, best-practice guides, and pattern libraries for analytics development across the organization.
  • Facilitate cross-functional analytics workshops and discovery sessions to identify new data sources, measurement plans, and instrumentation requirements.
  • Integrate third-party data sources (ad platforms, CRM, marketing automation, finance systems) into a unified analytics layer, ensuring mapping, cleansing, and lineage tracking.
  • Support forecasting and predictive modeling efforts by preparing feature sets, validating model outputs, and operationalizing model predictions into dashboards and downstream systems.
  • Maintain up-to-date metadata, lineage, and data catalog entries (e.g., Alation, Collibra, internal catalogs) to improve dataset discoverability and trust.
  • Develop automated testing frameworks for data pipelines and BI artifacts (unit tests, regression tests, snapshot tests) to prevent regressions and improve deployment reliability.
  • Provide hands-on support during incidents and outages related to analytics systems, perform root-cause analysis, and drive remediation efforts with engineering teams.
  • Evaluate and recommend new analytics tools, visualization patterns, and data platform capabilities to continuously improve time-to-insight and developer productivity.
  • Ensure analytics deliverables comply with data privacy regulations (GDPR, CCPA) and internal security standards, collaborating with security and legal teams when required.
  • Drive the adoption of self-service analytics by building templates, training sessions, and governance guardrails that empower non-technical users while preserving data integrity.

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 mastery for analytics: window functions, CTEs, performance tuning, and query optimization in cloud warehouses (Snowflake, BigQuery, Redshift).
  • Experience building interactive dashboards and visual analytics with Power BI, Tableau, or Looker, including advanced calculations and UX best practices.
  • Hands-on experience with data transformation frameworks / ELT tooling such as dbt, Airflow, Azure Data Factory, or equivalent orchestration tools.
  • Solid understanding of data modeling techniques: dimensional modeling, star/snowflake schemas, slowly changing dimensions, and fact table design.
  • Cloud data platform experience (AWS, GCP, or Azure) and familiarity with managed warehouse services (Snowflake, BigQuery, Redshift).
  • Programming proficiency in Python or R for data manipulation, automation scripts, and lightweight analytical tasks.
  • Experience integrating and transforming data from SaaS sources (Salesforce, Marketo, Google Analytics, ad platforms) via APIs or ingestion tools.
  • Familiarity with data governance tooling and practices: cataloging, metadata management, data lineage, and PII management.
  • Knowledge of data quality frameworks and testing approaches; ability to author unit and regression tests for ETL and BI artifacts.
  • Proficiency with version control (Git), CI/CD pipelines, and collaborative development workflows for analytics code.
  • Understanding of statistics and experimentation methodologies (A/B testing, hypothesis testing, basics of causal inference).
  • Dashboard performance optimization techniques, caching, and the use of materialized views or aggregated tables.
  • Experience with scripting for automation (Bash, PowerShell) and scheduling/monitoring analytics jobs.
  • Proficiency in DAX/MDX or LookML for semantic layer calculations (when applicable).
  • Familiarity with privacy and security compliance requirements relevant to analytics (GDPR, CCPA, SOC2).

Soft Skills

  • Strong business acumen with the ability to align analytics deliverables to strategic goals and KPIs.
  • Excellent stakeholder management and communication skills; able to convey technical concepts to non-technical audiences.
  • Problem-solving mindset with a structured, hypothesis-driven approach to investigation and root-cause analysis.
  • Collaboration and cross-functional teamwork orientation; experience working in agile environments.
  • Time management and prioritization skills, capable of balancing long-term projects with high-priority ad-hoc requests.
  • Attention to detail and a quality-first approach to analytics delivery.
  • Coaching and mentoring aptitude to help grow junior team members and promote best practices.
  • Adaptability to changing business priorities and evolving data landscapes.
  • Ethical judgment and a privacy-conscious approach to handling sensitive data.
  • Curiosity and a continuous learning mindset to stay current with analytics trends and tooling.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master’s degree in Data Science, Business Analytics, Statistics, Computer Science, or an MBA with strong analytics focus.

Relevant Fields of Study:

  • Computer Science
  • Data Science / Machine Learning
  • Statistics / Applied Mathematics
  • Business Analytics / Information Systems
  • Economics / Operations Research

Experience Requirements

Typical Experience Range:

  • 3–6 years of professional experience in analytics, business intelligence, or data engineering roles with demonstrable delivery of production dashboards and data products.

Preferred:

  • 5+ years of experience including proven ownership of end-to-end analytics solutions, experience in cloud data platforms, and a track record of translating analytics into measurable business impact.
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