๐Ÿค– Built With AI-Assisted Engineering

Most of my new work now gets built using AI-assisted development (Claude Code and similar tools). It takes a tool from idea to a working, deployed build in days instead of months, and that speed is part of what makes the pricing on the packages page realistic.

๐Ÿฉบ

Query Doctor

Paste a BigQuery SQL query, get an instant dry-run cost estimate, monthly cost projection, cache-hit rate, a 0โ€“100 optimization score, and AI-generated rewrite suggestions.

Next.jsFastAPIBigQueryDocker
๐ŸŸข Live & deployed
๐Ÿ“Š

BigQuery Cost & Spend Dashboards

Executive dashboards that read BigQuery's own usage metadata to show which queries are driving spend (Pareto view, KPIs, drill-down), with AI-generated optimization tips.

FastAPIINFORMATION_SCHEMAGPT-4oGemini
๐ŸŸข Live & deployed
๐Ÿ’ฐ

Monthly Cloud-Cost Checkup

Runs every month, quietly checks a cloud bill for waste (old snapshots, duplicate data, inefficient queries) and hands back a plain-English breakdown of exactly what's costing money and why.

๐Ÿ“ˆ Real result: one month's check caught $368/month ($4,416/year) in cloud spend that was quietly leaking out unnoticed.
BigQueryGoogle Sheets APIPython
๐ŸŸข Live & running monthly

A note on data handling: your files are never used to train any model, and everything I deliver is reviewed by me personally before it reaches you.

โšก Quick-Turnaround Data Jobs

The kind of scoped, "get this data usable fast" work I've done for outside vendors and partners: multi-format ingestion, cleanup, automation, and stable datasets, most delivered in 10โ€“15 days.

๐Ÿงฉ Pandalytics

Data analytics platform offering business intelligence APIs.

Integrated nested JSON data into GCP and BigQuery, enabling analytics-ready dashboards.

Cloud FunctionsBigQuery

๐ŸŒ Similarweb

Market intelligence platform offering traffic and engagement insights.

Automated cohort generation and dashboard updates on a weekly cadence.

PythonBigQueryCloud Scheduler

๐Ÿ—‚๏ธ DataProvider

Global web data provider delivering structured business intelligence datasets.

Merged monthly snapshots into dynamic views that feed straight into the dashboards.

Cloud StorageBigQuery Views

๐Ÿ“‘ Abuse & Compliance

Third-party security and audit service for digital compliance reporting.

Created compliance-ready reference datasets with document proofing capabilities.

Cloud FunctionsBigQuery

๐ŸŒ DNSLookup

DNS intelligence service offering real-time and historical DNS datasets.

Stored and optimized DNS data for scalable, big-data consumption.

Cloud StorageBigQuery

๐Ÿ” Namify

AI-powered domain name generator and branding platform.

Powered real-time domain name recommendations for Namify's platform.

BigQueryCloud Functions

๐ŸŒ ICANN Zone Data

Public registry of top-level domain zone files managed by ICANN.

Automated daily ingestion of ICANN zone files for monitoring and reporting.

Cloud SchedulerWorkflowsBigQuery

๐Ÿ—๏ธ Bigger Platform Work

The enterprise-scale side of my day job, in case your project ever grows past a spreadsheet or a small database.

BigQuery Migration + Cost Optimization

Cloud modernization + FinOps discipline · GCP ยท BigQuery ยท SQL ยท Tableau

Situation: Legacy on-premise infrastructure and mixed query patterns led to slow delivery and unpredictable costs.

  • Architected and executed the migration from on-premise to BigQuery
  • Designed migration governance: rollout planning, rollback strategy, change management
  • Introduced partitioning, clustering, and query tuning after the move
  • Added spend visibility, cost attribution, and anomaly-detection alerting
  • 30% lower infrastructure and query cost, 25% better platform availability, with zero production disruption
  • A further 40% cut in reporting latency once partitioning/clustering and FinOps guardrails were rolled out
  • $50K+ in annual cloud savings sustained through ongoing cost controls

Technical Debt Reduction + Platform Modernization

Reliability + clarity + ownership · BigQuery ยท Tableau ยท Governance

Situation: Years of incremental BI growth caused duplicated logic, fragmented pipelines, and maintenance drag.

  • Audited pipelines, scheduled queries, extracts, and "who owns what"
  • Standardized datasets and conventions (naming, lifecycle, documentation)
  • Implemented performance policies and quality scorecards
  • Lower compute waste via lifecycle policies and optimization patterns
  • Better reliability and less surprise breakage across reporting

Self-Serve BI Platform Launch

Reduce ad-hoc load, improve adoption · GCP ยท BigQuery ยท Tableau

Situation: Central BI teams were overloaded with recurring questions and custom extracts.

  • Built governed shared datasets and a BI hub
  • Defined access patterns and documentation
  • Created repeatable onboarding so teams could self-serve
  • 50% reduction in ad-hoc reporting requests
  • Faster insight turnaround and higher adoption: enterprise BI adoption up 50% across India, APAC and Europe

BI Extract Monitoring + Alert Automation

Operational BI reliability · BigQuery metadata ยท n8n ยท Tableau

Situation: Extract failures and stale dashboards created downtime and invisible data drift.

  • Built monitoring signals from metadata and usage patterns
  • Automated alerting and routing (team-appropriate notifications)
  • Reduced manual chasing and improved response time
  • Fewer incidents and faster detection, no more chasing broken dashboards after the fact

AI-Powered Industry News Automation

Internal enablement via automation · n8n ยท LLM summarization ยท Slack/email

Situation: Stakeholders needed a daily signal without manual curation.

  • Aggregated sources, summarized, tagged, and distributed updates
  • Owned orchestration, scheduling, and delivery pipeline
  • Cut manual curation work by about 90%. The daily update runs itself now

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