Role Overview
The Data Analyst is responsible for turning Inventeer’s operational and product data into decision-ready insights that improve prioritization, delivery predictability, and product outcomes.
This is a hands-on role. You will define KPIs, design dashboards, and deliver recurring reporting used by leadership and day-to-day operators. Success is measured by clarity, trust in metrics, actionable insights, and systems that keep working (not one-off reports).
Key Responsibilities
Data Quality, Modeling & Sources of Truth
- Collect, clean, validate, and reconcile datasets to ensure consistency and reliability.
- Define and document KPI logic, metric definitions, dimensions, and sources of truth.
- Establish consistent naming conventions and metric governance so teams stop debating numbers.
Dashboards, Reporting & Visibility Systems
- Design, build, and maintain dashboards and recurring reports for different audiences:
- leadership (strategy + forecasting),
- delivery operations (throughput, bottlenecks, cycle time),
- product (adoption, engagement, quality signals),
- client-facing reporting when applicable.
- Ensure dashboards are understandable, durable, and tied to decisions (not vanity metrics).
Analysis & Decision Support
- Perform analysis to identify trends, anomalies, and root causes (e.g., delivery delays, defect patterns, adoption drop-offs).
- Translate analysis into clear narratives and recommendations with next steps.
- Provide decision support: what’s happening, why it matters, what we should do now.
Instrumentation & Analytics Enablement
- Partner with Engineering to improve instrumentation (events, logs, tracking, data capture) and data integrity.
- Define what should be tracked and how, with pragmatic implementation guidance.
- Validate reporting pipelines and ensure metric continuity when systems change.
Ownership & Continuous Improvement
- Proactively identify gaps in visibility and propose improvements (new metrics, better tracking, better dashboards).
- Drive work through to completion, including documentation, rollout, and stakeholder adoption.
Required Technical Skills
- Strong analytical ability: end-to-end problem solving using real datasets (not only “dashboard building”).
- Solid applied statistics for business/product questions (trends, anomalies, segmentation, correlation vs causation awareness).
- Experience with KPI/metric design: definitions, documentation, governance, and dimensional modeling concepts.
- Strong PostgreSQL skills (required).
- Experience building dashboards for multiple audiences using BI tools (Metabase / Tableau / Looker / Power BI or equivalent).
- Strong communication skills: can present insights clearly to technical and non-technical stakeholders.
- High ownership mindset: outcomes > activity.
Nice to Have
- Python for analytics automation (pandas), lightweight pipelines, or reporting utilities.
- Experience analyzing delivery/operations data (cycle time, throughput, WIP, SLA patterns).
- Familiarity with product analytics concepts (activation, retention, cohort analysis, funnels).
- Experience defining event tracking schemas and validating analytics instrumentation.
- Experience with data quality practices (tests, reconciliation checks, anomaly detection).
Behavioral Expectations
- Truth-seeking: prioritizes accuracy and clear definitions over “nice charts.”
- Decision orientation: every output connects to an action, a choice, or a tradeoff.
- Ownership: closes loops—metrics stay maintained, dashboards stay trusted.
- Clarity: writes definitions and narratives that reduce confusion and repeated questions.
- Collaboration: works tightly with Engineering/Product/Ops to improve instrumentation and adoption.
- Pragmatism: builds the simplest system that provides reliable visibility and scales.