Case Studies
Real platform work (not demo projects). Metrics are representative of delivered outcomes.
1) BigQuery Migration + Cost Optimization
Theme: Cloud modernization + FinOps discipline
Stack: GCP • BigQuery • SQL • Tableau
Situation
Legacy warehouse + mixed patterns led to slow delivery and unpredictable query costs.
What I did
- Re-designed data model + ETL/ELT flows
- Introduced partitioning, clustering, and query tuning
- Added spend visibility, usage-based attribution, and alerting
- Set governance patterns so the savings stayed
Outcomes
- ⏱️ ETL processing improved (reported up to 80% faster in critical paths)
- 💸 Reduced query/storage cost via optimization patterns (reported ~30–40% on heavy workloads)
- 🧭 Established ongoing cost controls and monitoring
2) Technical Debt Reduction + Platform Modernization
Theme: Reliability + clarity + ownership
Stack: BigQuery • Tableau • Governance
Situation
Years of incremental BI growth caused duplicated logic, fragmented pipelines, and maintenance drag.
What I did
- Audited pipelines, scheduled queries, extracts, and “who owns what”
- Standardized datasets and conventions (naming, lifecycle, documentation)
- Implemented performance policies and quality scorecards
- Reduced duplication and improved trust
Outcomes
- 📉 Lower compute waste via lifecycle policies + optimization patterns
- ✅ Better reliability and “less surprise breakage” across reporting
3) Self-Serve BI Platform Launch
Theme: Reduce ad-hoc load, improve adoption
Stack: GCP • BigQuery • Tableau
Situation
Central BI teams were overloaded with recurring questions and custom extracts.
What I did
- Built governed shared datasets and a BI hub
- Defined access patterns and documentation
- Created repeatable onboarding so teams could self-serve
Outcomes
- 🙅 Reduced ad-hoc requests (reported ~50% in rollout areas)
- ⚡ Faster insight turnaround and higher adoption :contentReference
4) BI Extract Monitoring + Alert Automation
Theme: Operational BI reliability
Stack: BigQuery metadata • n8n • Tableau
Situation
Extract failures and stale dashboards created downtime and invisible data drift.
What I did
- Built monitoring signals from metadata + usage patterns
- Automated alerting and routing (team-appropriate notifications)
- Reduced “manual chasing” and improved response time
Outcomes
- 🛠️ Lower incident load and faster detection (reported improvements in downtime reduction)
5) AI-Powered Industry News Automation
Theme: Internal enablement via automation
Stack: n8n • LLM summarization • Slack/email
Situation
Stakeholders needed a daily signal without manual curation.
What I did
- Aggregated sources, summarized, tagged, and distributed updates
- Owned orchestration + scheduling + delivery pipeline
Outcomes
- 🧠 Reduced manual curation effort (reported ~90%)
Fast-turnaround vendor integrations (10–15 day builds)
When the need is “get this data usable fast”: multi-format ingestion, automation, and stable datasets.
🧩 Pandalytics
- About the Vendor: Data analytics platform offering business intelligence APIs.
- Tools Used: Cloud Functions, BigQuery
- Outcome: Integrated nested JSON data into GCP and BigQuery, enabling analytics-ready dashboards.
🌐 Similarweb
- About the Vendor: Market intelligence platform offering traffic and engagement insights.
- Tools Used: Python, BigQuery, Cloud Scheduler
- Outcome: Automated cohort generation and dashboard updates on a weekly cadence.
🗂️ DataProvider
- About the Vendor: Global web data provider delivering structured business intelligence datasets.
- Tools Used: Cloud Storage, BigQuery Views
- Outcome: Merged monthly snapshots into dynamic views for seamless dashboard integration.
📑 Abuse & Compliance
- About the Vendor: Third-party security and audit service for digital compliance reporting.
- Tools Used: Cloud Functions, BigQuery
- Outcome: Created compliance-ready reference datasets with document proofing capabilities.
🌐 DNSLookup
- About the Vendor: DNS intelligence service offering real-time and historical DNS datasets.
- Tools Used: Cloud Storage, BigQuery
- Outcome: Stored and optimized DNS data for scalable, big data consumption.
🔍 Namify
- About the Vendor: AI-powered domain name generator and branding platform.
- Tools Used: BigQuery, Cloud Functions
- Outcome: Powered real-time domain name recommendations for Namify’s platform.
🌐 ICANN Zone Data
- About the Vendor: Public registry of top-level domain zone files managed by ICANN.
- Tools Used: Cloud Scheduler, Workflows, BigQuery
- Outcome: Automated daily ingestion of ICANN zone files for monitoring and reporting.