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SkillsMP has collected 24 skills from hollandkevint/data-product-operator. Open a skill to review its source and details.
Showing 24 of 24 collected skills.
Build or update an evidence graph for a data analysis by linking the question, definitions, source versions, checks, findings and decisions. Use when an analysis spans multiple skills or someone asks what supports a conclusion, what changed, or what to check…
Automated data quality checks for pipelines. Testing pyramids, dbt test patterns, data contracts, circuit breakers, and monitoring. Use when implementing data quality checks, writing dbt tests, defining data contracts, setting up pipeline validation, building…
Help a data leader or data product manager choose the next decision, coordinate delivery and review outcomes across one product or a portfolio. Use to establish or improve a data-product operating rhythm, connect existing skills and preserve decision context.…
Team structure and operating rhythm for data product teams. Product squads, Shape Up 6-week cycles, handoff contracts, and role assignments by lifecycle stage. Use when organizing a data team, planning data product development cycles, defining roles and…
Review a Databricks data product or proposed release using approved metadata and query evidence. Use for Unity Catalog scope, Delta change handling, reconciliation, or a release handoff. Does not provision infrastructure or authorize production writes.
Profile approved CSV or Parquet files with DuckDB and produce reproducible quality and join checks. Use for local exploratory analysis, schema drift, identifier preservation or source reconciliation. Does not authorize sensitive-data downloads or arbitrary…
Turn a healthcare population question into a reviewable cohort specification and acceptance cases. Use for claims or EHR cohorts, OMOP studies, quality-reporting denominators or trial-feasibility counts. Does not determine clinical trial eligibility or supply…
Healthcare data domain context covering FHIR, HL7, OMOP CDM, real-world evidence, and clinical terminology systems. Use when working on clinical data pipelines, EHR integrations, claims data products, HIPAA-governed data, OMOP transformations, or when the…
Debrief one healthcare data project where preparation took more work than expected. Use a project summary or short interview to identify evidence gaps, trace their effect on the intended use, and produce a practical next-check brief. Works without patient…
Investigate disagreements between healthcare source records and downstream outputs. Use for claims/RCM totals, HL7/FHIR record pulls, mappings, clinical-result routing or patient-operations state. Produce an evidence trace and discriminating checks without…
Convert raw discovery notes into structured insights using atomic research methods adapted for data products. Use when synthesizing findings, reviewing evidence, summarizing research, writing problem briefs, or when someone asks "what did we learn?" or "what…
3 diagnostic questions for evaluating data product markets through the arbitrage gap lens. Identifies whether your data product sits on a durable or closing advantage. Use when assessing data product positioning, evaluating market risk, or when someone asks…
Convert dashboard requests into decision specifications. The missing layer between "build me a dashboard" and "help me decide." Use when receiving dashboard requests, reviewing analytics backlogs, prioritizing data team work, or when someone asks "what…
Decision tree exploration for data product plans. Interview relentlessly about schema decisions, consumer contracts, quality SLAs, and delivery choices. Use when planning a data product, designing a schema, choosing a delivery method, or when someone asks…
Discover what internal data consumers actually need. Adapted Mom Test and JTBD for data teams. Use when conducting user research, interviewing stakeholders, gathering consumer requirements, running discovery sessions, or when someone asks "what do they need?"…
First-principles reasoning for data product decisions. Frames problems as data products, not dashboards or pipelines. Use when evaluating data product strategy, making build-vs-buy decisions, scoping data product features, assessing product-market fit for…
Score whether a data product idea is worth building before committing resources. Validation scorecard, experiment design, and go/kill decisions. Use when evaluating feasibility, making go/no-go decisions, validating demand, sizing bets, or when someone asks…
Position data teams as strategic partners, not order-takers. The organizational "why" behind doing discovery work. Use when discussing team positioning, value exchange, demand shaping, escaping the order-taker trap, or when someone asks "how do we stop being…
Dimensional modeling and schema design for data products. Star schema patterns, slowly changing dimensions, denormalization decisions, and architecture decision records. Use when designing data models, reviewing schema designs, choosing between normalization…
Data presentation and storytelling for data product operators. Narrative structures, chart selection, headline formulas, and anti-patterns. Use when presenting data to stakeholders, building a data presentation, writing an executive summary of findings,…
Precise metric definitions for data products. Outcome metric trees, naming conventions, grain specification, and the "what does this number mean?" problem. Use when defining KPIs, writing metric specifications, resolving conflicting metric definitions,…
Systematic data quality evaluation covering completeness, accuracy, timeliness, consistency, and validity. Use when assessing data pipelines, reviewing data product quality, auditing data sources, defining quality SLAs, building data quality monitors, or when…
Ethical data risk evaluation, bias testing protocols, and governance practices for data products. Use when evaluating ML/AI features for fairness, designing bias testing protocols, planning phased rollouts for high-risk changes, reviewing data governance…
Translates between technical data teams and business stakeholders. Use when preparing stakeholder updates, translating technical data work for executives, shaping vague business requests into buildable specs, navigating competing priorities across data…