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Key Responsibilities and Required Skills for Insight Technician

💰 $45,000 - $75,000

Data & InsightsAnalyticsField Operations

🎯 Role Definition

The Insight Technician is a hands-on analytics and operations professional who collects, validates, transforms, and communicates data to produce actionable insights for product, marketing, operations and business teams. This role blends field or system-level data collection and maintenance with analytical and reporting responsibilities: designing and executing measurement plans, performing data quality assurance, building dashboards and automated reports, and working directly with stakeholders to translate questions into measurable outcomes. The ideal candidate is technically fluent (SQL, Excel, BI tools), experienced in data hygiene and tagging, comfortable with basic statistical tests and A/B experimentation, and adept at communicating findings to non-technical audiences.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data or Analytics Assistant / Junior Data Analyst
  • Field Technician or Instrumentation Technician with data exposure
  • Market Research or Insights Coordinator

Advancement To:

  • Senior Insight Technician / Senior Data Analyst
  • Insights Analyst / Business Intelligence Analyst
  • Analytics Engineer or Data Engineer (with further technical training)

Lateral Moves:

  • Product Analyst
  • Marketing Analytics Specialist
  • Operations Analyst

Core Responsibilities

Primary Functions

  • Collect, ingest and validate raw data from multiple sources (CRM, web analytics, sensors, third-party providers) using repeatable ETL processes and documented scripts to ensure reliable inputs for insight generation.
  • Design, implement and maintain tagging, tracking and instrumentation plans (web, mobile, POS) to support accurate event capture and downstream attribution for marketing and product analytics.
  • Perform cleansing, normalization and enrichment of datasets, handling time series, categorical mappings and deduplication to maintain consistent dimensions and KPIs across reports.
  • Write, review and optimize SQL queries to extract, aggregate and join data from relational data warehouses and cloud data stores (e.g., Redshift, BigQuery, Snowflake) for analysis and dashboarding.
  • Build, maintain and optimize interactive dashboards and visualizations in Power BI, Tableau or Looker, focusing on performance, UX, and interpretation for executive and operational stakeholders.
  • Produce scheduled and ad-hoc reports that translate business questions into measurable metrics, including cohort analyses, funnel diagnostics and trend identification with clear conclusions and recommended next steps.
  • Conduct routine data quality audits and reconciliation between source systems and reporting layers; identify root causes of discrepancies and coordinate fixes with engineering and data teams.
  • Support A/B test setup, tagging, sample allocation and post-experiment analysis; compute lift, confidence intervals and create experiment reports with actionable recommendations.
  • Implement and monitor data pipelines using ETL/ELT frameworks and orchestration tools (Airflow, Prefect, dbt), ensuring job reliability, alerting and recovery procedures are in place.
  • Automate repetitive reporting tasks using scripting (Python, R, or SQL-based macros) and deployed routines to reduce manual effort and increase report cadence.
  • Produce and maintain technical documentation and runbooks for dataset definitions, transformation logic, dashboard instructions and standard operating procedures to enable team scale and reduce institutional risk.
  • Partner with product, marketing, sales and operations teams to scope measurement requirements, translate business hypotheses into analytics specifications and prioritize dashboard and report development.
  • Validate third-party data feeds (advertising platforms, analytics vendors, survey providers) for consistency, latency and integrity; negotiate remediation with vendors when necessary.
  • Instrument and maintain IoT or field data collection hardware and software where applicable: schedule calibrations, perform basic troubleshooting, and ensure secure transmission of readings for ingestion.
  • Monitor KPIs and set up automated alerts and anomaly detection to proactively surface business risks and opportunities to stakeholders.
  • Execute exploratory data analysis and hypothesis-driven investigations to uncover root causes, outliers and patterns that inform product and operational improvements.
  • Provide first-line support to business users for data requests, basic dashboard training, and interpretation of metrics; escalate complex analytics to senior analysts or data scientists.
  • Maintain compliance with data privacy regulations and internal governance, applying PII handling rules, anonymization and access controls in datasets and reports.
  • Coordinate with engineering, DevOps and vendors to resolve pipeline failures, schema changes and system upgrades that impact data availability or accuracy.
  • Drive continuous improvements to measurement frameworks and reporting processes, soliciting stakeholder feedback and iterating on deliverables to increase impact and usability.
  • Use statistical methods (t-tests, regression basics, confidence intervals) to quantify effects and support data-driven decision making while clearly communicating limitations, assumptions and potential biases.
  • Assist in recruitment, onboarding and mentorship of junior insight staff and technicians; share best practices for data collection, QA and reporting.

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)

  • SQL: advanced query writing, joins, window functions and performance tuning for warehouse environments.
  • Data visualization: authoring dashboards and visual stories in Power BI, Tableau or Looker with best-practice UX and calculated fields.
  • Scripting and automation: Python or R for ETL scripts, transformations, scheduled jobs and simple statistical analysis.
  • Data modeling and ETL/ELT concepts: schema design, normalization, denormalization and use of tools such as dbt, Airflow or similar.
  • Web & mobile analytics: Google Analytics (GA4), Adobe Analytics, event tagging, and measurement plan implementation.
  • Experimentation: A/B test setup and analysis, sample sizing basics, interpretation of lift and statistical significance.
  • Data quality and validation: reconciliation, anomaly detection, logging and root cause analysis for data issues.
  • Cloud data platforms: familiarity with BigQuery, Snowflake, Redshift or comparable data warehouses.
  • Excel & advanced spreadsheets: pivot tables, advanced formulas, Power Query for quick analysis and ad-hoc tasks.
  • SQL-based BI tool integration and embedding: ability to connect queries to dashboards and optimize refresh times.
  • Basic statistics: hypothesis testing, descriptive statistics, confidence intervals and basic regression interpretation.
  • Data governance & privacy: understanding of PII handling, anonymization techniques and access controls.
  • API integration & third-party connectors: ingesting marketing, CRM or telemetry data through APIs and connectors.
  • Field hardware maintenance (where applicable): basic troubleshooting/calibration for sensors and data loggers.

Soft Skills

  • Strong communication: translate complex analyses into concise, actionable insights for non-technical stakeholders.
  • Stakeholder management: prioritize competing requests, negotiate timelines and set clear expectations.
  • Attention to detail: meticulous verification of metrics and reporting logic to maintain credibility.
  • Problem-solving mindset: investigative approach to diagnose data issues and recommend durable fixes.
  • Time management: balancing recurring reporting, maintenance work and high-priority ad-hoc requests.
  • Collaboration: work cross-functionally with product, engineering, marketing and operations to align measurement and reporting goals.
  • Adaptability: comfortable learning new tools, changing data sources, and evolving measurement frameworks.
  • Documentation discipline: habit of creating and maintaining clear runbooks and data definitions.
  • Customer-focus: proactively seek feedback and refine deliverables to meet user needs.
  • Teaching/coaching: ability to upskill colleagues on basic analytics practices and dashboard usage.

Education & Experience

Educational Background

Minimum Education:

  • Associate degree or technical diploma in data analytics, computer science, information systems, statistics, or related field; or equivalent practical experience.

Preferred Education:

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

Relevant Fields of Study:

  • Data Science / Analytics
  • Computer Science
  • Statistics / Mathematics
  • Business Analytics / Economics
  • Engineering / Instrumentation (for field-focused roles)

Experience Requirements

Typical Experience Range: 1–5 years of combined experience in data collection, analytics, dashboarding or field data operations.

Preferred:

  • 2+ years working with SQL and at least one BI platform (Power BI, Tableau, Looker).
  • Prior experience managing tagging or instrumentation for web/mobile or handling time-series sensor data.
  • Demonstrable history of delivering dashboards and operational reports used by business stakeholders.
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