SpaceX Market Data Accuracy Repair
Budget: $100,000 – $0 CAD
Our market-facing analytics are reporting conflicting numbers and I need a sharp data specialist to hunt down and eliminate the discrepancies. The problems sit squarely in the data analytics layer: customer records don’t balance with financial reports, operational KPIs drift between refreshes, and leadership is losing confidence in the dashboards.
I have already confirmed that accuracy issues are present in all three source domains—customer, financial, and operational—so the task is to trace the pipelines end-to-end, pinpoint root causes, and deliver a rock-solid single source of truth.
Here’s what I expect from you:
• Audit current ETL jobs, warehouse schemas and any ad-hoc queries touching those datasets.
• Fix or rebuild faulty transformations, making sure every business rule is documented in-line or in Git.
• Implement automated validation tests that flag anomalies before they reach Power BI/Tableau dashboards.
• Provide a concise hand-over report explaining what was wrong, how you patched it, and how to keep it stable.
Acceptance criteria
• Customer, financial and operational tables reconcile within 0.1 % of their respective authoritative systems.
• All pipelines run reliably on schedule for one week without manual intervention.
• Unit tests and data-quality alerts are live in CI/CD so future changes can’t reintroduce errors.
I’m ready to grant access to the warehouse (Snowflake), our Python-based Airflow DAGs and the dashboard layer the moment we agree on an approach. Let’s get SpaceX leadership trusting the numbers again.
I have already confirmed that accuracy issues are present in all three source domains—customer, financial, and operational—so the task is to trace the pipelines end-to-end, pinpoint root causes, and deliver a rock-solid single source of truth.
Here’s what I expect from you:
• Audit current ETL jobs, warehouse schemas and any ad-hoc queries touching those datasets.
• Fix or rebuild faulty transformations, making sure every business rule is documented in-line or in Git.
• Implement automated validation tests that flag anomalies before they reach Power BI/Tableau dashboards.
• Provide a concise hand-over report explaining what was wrong, how you patched it, and how to keep it stable.
Acceptance criteria
• Customer, financial and operational tables reconcile within 0.1 % of their respective authoritative systems.
• All pipelines run reliably on schedule for one week without manual intervention.
• Unit tests and data-quality alerts are live in CI/CD so future changes can’t reintroduce errors.
I’m ready to grant access to the warehouse (Snowflake), our Python-based Airflow DAGs and the dashboard layer the moment we agree on an approach. Let’s get SpaceX leadership trusting the numbers again.
Related categories:
Python
Data Processing
Data Warehousing
Data Analytics
Data Analysis
Data Integration
Power BI
ETL