Vansh BData Engineer

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Pipelines thatnever losea row.

Custom ETL over inconsistent sources, analytical ClickHouse and Postgres tuned to the query, ingestion that reruns safely. Schema to dashboard, I own the plumbing.

6B+

rows in production ClickHouse

750+

matches ingested automatically

500ms

P90 analytical query latency

About me

I am Vansh Bordia, a data engineer who likes the boring parts: idempotent jobs, validated schemas, and a pipeline that can be rerun at 2am without anyone sweating. Half a terabyte of raw broadcast data became structured analytics because the ingestion was built to survive messy sources.

I care about the boring parts because they are what makes the numbers trustworthy downstream: reconciliation checks, retry and rate-limit logic, columnar storage tuned to the actual query pattern, and documentation so the next person can extend it.

Why me

I build the pipes, not just the queries: multi-source ETL that reconciles inconsistent schemas, analytical storage tuned to the query pattern, and ingestion that has not lost a row in production.

I build the pipes, not just the queries. Ingesting multi-source broadcast JSONL with inconsistent schemas, modelling it for analytical workloads, and keeping the pipeline idempotent so a rerun never corrupts a dashboard. 6B+ rows in production ClickHouse at sub-500ms P90.

6B+ rows in production ClickHouse at sub-500ms P90

Custom ETL reconciling inconsistent multi-source schemas

750+ matches ingested automatically, validated before publish

Role stack

Python / SQL / ClickHouse / PostgreSQL / Apache Spark / Kafka / ETL Orchestration / Data Modeling / Schema Validation / Query Optimization / Redis / Docker / Kubernetes / Data Quality / Power BI / Google Analytics 4 / Looker Studio / Excel

Relevant work

RiftWatch

Esports intelligence platform, acquired by a pro org

  • 750+ pro matches processed into 6B+ analytical rows in ClickHouse
  • 20 orgs at peak including Tier-1 across Americas and EMEA
  • VALORANT-specific Gemma models plus RAG over live patch data
  • Agentic harness routing tools, models and queries; sub-500ms P90
PythonFastAPIClickHousePostgreSQLRedisKubernetesReact
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VLR Dev API

Type-safe Python library for Valorant data, published on PyPI

  • Type-safe Python library for Valorant data, published on PyPI
  • Retry logic, rate limiting and schema validation built in
  • Earned official Riot data API access in 2 weeks, not 9 months
PythonPyPIREST
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DDL Showcase

Interactive SQL schema visualizer, 100% client-side

  • Dual-dialect SQL parser, PostgreSQL plus ClickHouse
  • Interactive ER diagrams with auto-layout and relationship inference
  • 100% client-side, data never leaves the browser
TypeScriptReactSQL
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ACF Import/Export

Production WordPress plugin in the official directory

  • Production WordPress plugin in the official directory
  • Secure file handling and sanitization per WP standards
PHPMySQLWordPress
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Contact

Need pipelines you can trust at 2am? Talk to me.

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