Data Processing & Reporting Checklist for SaaS & Startups

Document expected columns, datatypes, nullability, delimiters, encodings, and sample records for each CSV, JSON, or PDF source, then enforce schema checks on ingest. Reject on violation, log errors with actionable messages, and notify owners to prevent downstream surprises.

Use AWS S3 or GCS with landing, raw, and processed folders, paired with lifecycle rules and immutability for auditability. Keep quarantined files separate, write atomic loads, and record manifest files so teams can trace exactly what was processed.

Apply a consistent naming convention, cast types explicitly, and unify units, currencies, and timezones early using pandas, Polars, or dbt. Store a mapping file that converts vendor-specific quirks into your canonical model for long-term maintainability.

Transform CSVs into Parquet backed by Arrow schemas to shrink storage, speed queries, and reduce type confusion. Maintain compatibility with DuckDB, BigQuery, or Spark, and bake schema evolution rules to handle added columns without breaking consumers.

Use pdfplumber, Tabula, Camelot, or Amazon Textract for scanned PDFs, then validate extracted tables against known totals and column patterns. Store confidence scores and fall back to manual review for low quality scans to avoid silent data corruption.

Add filename, URI, source system, load timestamp, content hash, schema version, and row counts to a metadata table. Use OpenLineage or dbt metadata tracking so analysts can answer where a number came from and what assumptions were applied.

Append firmographic data using APIs like Clearbit, Apollo, or Crunchbase, then standardize industry, size, and domain fields. Track enrichment vendor, timestamp, and coverage rate, and build fallbacks to maintain consistent attributes when APIs throttle or fail.

Enrich IPs using ipinfo.io or MaxMind GeoLite2, then map users to timezones and regions for more accurate session and notification windows. Persist derived geos with versioned reference data to keep cross-region comparisons consistent over time.

Unify users and accounts from Segment, Snowplow, Stripe, and Salesforce using email domains, hashed identifiers, and fuzzy matching with Dedupe.io or Splink. Create deterministic keys, track match confidence, and store a mapping table to stabilize reports.

Extract utm_source, utm_medium, utm_campaign, and referrers, then map them to canonical channels and campaigns with a controlled vocabulary. Validate campaign names, strip noise parameters, and tie sessions to conversions for clean CAC and ROI reporting.

Fetch daily FX rates from ECB or OpenExchangeRates, then convert transactions to a base currency at invoice date. Store applied rate, source, and effective date to avoid month-end discrepancies, and lock historical periods to prevent drift.

Group events into sessions using inactivity thresholds, then tag features based on event names, flags, or properties from Segment, RudderStack, or Snowplow. Persist durable session IDs and feature adoption metrics that tie cleanly to activation and retention.

Write expectations for schema, uniqueness, ranges, foreign keys, and aggregations, then run them on every load. Gate downstream jobs on validation results, store snapshots of violations, and continuously refine checks to reflect real business rules.

Track last successful load timestamps and row counts, then notify owners via Slack or PagerDuty when freshness or volume deviates. Assign clear SLAs to high value tables like revenue and activation so executives trust summaries at a glance.

Run Z-score, IQR, or time series models like Prophet to catch usage spikes, billing anomalies, or ingestion drops. Quarantine suspect rows with reason codes, then provide override mechanics so domain experts can mark legitimate exceptions.

Tokenize emails and phone numbers, store secrets in HashiCorp Vault, and push row or field level permissions in the warehouse. Maintain masked views for analysts and define explicit PII handling playbooks to avoid accidental exposure in exports.

Use keyed upserts and staging tables, verify counts before swaps, and rerun deterministic transforms without double counting. Leverage dbt snapshots or merge statements to handle late arriving data while keeping derived metrics stable.

Adopt lakeFS or Delta Lake for dataset versioning, then pin reports to snapshot IDs to make board-level numbers auditable. Record transform code versions and environment details so analysts can re-run old reports with identical results.

Document ARR, MRR, NRR, churn, activation, and LTV with clear time windows and inclusions, then encode them in dbt metrics or LookML. Provide unit tests for KPI logic and sample records to keep cross-team reports in lockstep.

Render HTML and PDFs using Jinja2 plus WeasyPrint, blend metric tables with benchmark deltas, highlights, and risk flags. Include sparklines and YoY comparisons, and make narratives parameterized so they update cleanly when datasets refresh.

Use Metabase, Looker, or Mode to deliver tailored views like feature adoption, funnel conversion, and pipeline velocity. Add drill-through paths and row-level filters so managers can resolve questions without pulling ad hoc data.

Define standards, line charts for trends, bars for categorical comparisons, stacked bars only when shares matter, funnels for stage conversions. Enforce defaults via Plotly or Altair configs to eliminate noisy visuals and make reports consistent.

Trigger deliveries with Slack webhooks, scheduled emails, and updates to Notion or Confluence spaces, then capture recipients and open rates. Respect access roles, hide sensitive tabs, and include deep links back to data sources for self-serve exploration.

Generate bullet points, top-line metrics, and confidence notes for changelogs and board decks via Google Slides API or Notion API. Keep messages short and accompanied by links to reproducible tables to reduce follow-up questions.

Store high-water marks, process only new or changed records, and handle CDC streams via Debezium or Kafka sinks. Keep audit tables of processed batches to avoid rework and guarantee predictable run times as data grows.

Favor DuckDB for local analytics, BigQuery or Snowflake for warehouse scale, and Parquet plus Arrow for fast interchange. Benchmark common queries and materialize summaries for MRR, active users, and funnel steps where latency impacts decision making.

Avoid Python loops, use joins, groupbys, and window functions, and push heavy work down to the warehouse when possible. Profile hotspots, then rewrite critical paths to columnar operations to cut costs and reduce runtime variance.

Set Snowflake warehouse sizes, BigQuery reservation slots, and per-project tags to track spend, then alert when jobs exceed budgets. Cache expensive aggregates and use partition pruning to keep routine reporting cheap and predictable.

Use exponential backoff, idempotent writes keyed by natural IDs, and dead-letter queues with SQS or Pub/Sub for bad messages. Isolate faulty batches, log error payloads, and allow safe replays so operations can recover without manual patching.

Publish run metrics and logs to Prometheus and Grafana, trace jobs with OpenTelemetry, and monitor error rates and durations over time. Capture SLA breaches, mean time to recover, and dataset-level health scores to prioritize fixes that matter.