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Dorwar

Data & Systems

Services

Data work that stays close to the problem.

The emphasis is on readiness, analytical clarity, and dependable reporting rather than broad transformation language.

Data Readiness

Typical stack: SQL, BigQuery, warehouse modeling

Profiling, validation, and cleanup work that makes source data usable for reporting and downstream analysis.

Teams with growing reporting needs, uneven source quality, or cloud data work that still needs a reliable analytical base.

Typical Problems

  • Source tables exist, but confidence in joins, fields, and definitions is still low
  • Repeated cleanup work is slowing down analysis and reporting
  • Different teams are using slightly different versions of the same metric

Typical Outputs

  • Data profiling and source-to-target validation
  • Reusable SQL transformations
  • Metric definitions aligned with business use
  • Clean handoff for reporting and dashboard work

Best suited to teams that need a more dependable base before analytics work spreads across the organization.

Analytical Workflows

Typical stack: SQL, Airflow, BI tooling, stakeholder analysis

Structured analytical workflows for recurring business questions across finance, operations, and product contexts.

Teams that want clearer analytical logic, more stable reporting cycles, and fewer ad hoc decisions hidden in spreadsheets.

Typical Problems

  • Business questions are answered repeatedly with slightly different logic each time
  • Reporting processes depend on brittle manual steps
  • Stakeholders need explanations that connect numbers back to system behavior

Typical Outputs

  • Recurring analytical models and reporting logic
  • Clear business rules and edge-case handling
  • Documentation for ownership and maintenance
  • Outputs shaped for finance, operations, or reporting teams

Works best when the problem is partly technical and partly operational, and both sides need to stay legible.

Scale and Reconciliation

Typical stack: Spark, PySpark, SQL, batch pipelines

Support for larger, messier, or high-consequence datasets where reconciliation and preparation need more structure.

Finance and operations environments where mismatches, timing gaps, or dataset size make lightweight workflows fragile.

Typical Problems

  • Exceptions are found late, after reports or downstream processes have already moved forward
  • Historical or partner data arrives with inconsistent structure
  • Preparation work is too heavy for a simple analyst-only workflow

Typical Outputs

  • Preparation flows for larger datasets
  • Reconciliation and quality checks
  • Curated datasets for downstream reporting
  • Notes on assumptions, failure points, and refresh behavior

Useful when scale or operational risk requires more structure, but the goal remains clear analysis and dependable reporting.