Signature Initiative

AI-integrated BigQuery SDLC — a repo-first operating model

Outcome ~5 days → 1–2 days Adopted as the org's reference pattern

Self-driven initiative to make AI pair-programming safely usable on a warehouse platform that had no native repo support and zero margin for unreviewed change. Now the reference pattern other Ashley teams are pointed to for their own data platforms.

Problem

  • Ashley pushed AI-first SDLC across engineering, but BigQuery (and warehouse platforms generally) had no native or third-party way to live in a Git repo — the very context AI tools need to be useful.
  • Leadership had legitimate concerns about AI touching databases: unintended schema or data changes are not as reversible as code, and human accountability had to be preserved.
  • Teams working on APIs, frontends and backends were accelerating with AI. Data engineering was being left behind.

Approach

  • Exported BigQuery into a repo: wrote an INFORMATION_SCHEMA query that emits every table, view, routine and function as SQL files into GCS, then materialised them into a proper repository structure with parallel JSON metadata for AI context.
  • Guardrails for the AI: authored an RFC-2119 style standards document (naming, audit columns, formatting, partitioning, JOIN discipline, documentation) that Augment Code is required to follow on every change.
  • Guardrails for humans: a development workflow that forbids direct DDL on any BigQuery project; all structural changes flow through feature branch → PR review → generated change script with explicit data-migration steps → deployment by named BigQuery owners.
  • Environment promotion: main-dev mirrors the dev GCP project; release branches into main mirror prod. Project IDs and dataset names are parameterised so the same artefacts work cleanly across environments.

Outcome

  • ~5 days → 1–2 days for representative use cases inside my team after adoption.
  • Endorsed by leadership after the POC and rolled out at the team level; subsequently held up as the reference pattern other Ashley teams follow for their databases.
  • Zero loss of accountability: every change is still gated by human PR review and executed by a small set of BigQuery owners, addressing the original leadership concern by design rather than by policy.
  • Currently evolving toward CI/CD on PR merge to remove manual deployment risk while preserving the PR-level human gate.

BigQuery · GCS · Augment Code · Git · GitHub PRs · INFORMATION_SCHEMA · PowerShell · JSON schemas

Reference Architecture

Near real-time CDC pipeline at enterprise scale

Problem. Operational systems held the truth, but analytics consumers needed fresh, query-ready data at 15 TB+ scale without straining the source databases or losing change events on failure.

Approach. Capture row-level change events at the source, fan them through a managed event bus, transform with a streaming engine, and land curated tables in the warehouse with full lineage, retries, and KQL-based alerting baked into delivery.

Outcome. A repeatable pattern that moved change events from source to dashboard in near real time, kept producer load steady, and exposed every failure through actionable alerts instead of silent drift.

  • FreshnessSeconds to single-digit minutes
  • Scale15 TB+ active workloads
  • ReliabilityAt-least-once, idempotent merge
  • ObservabilityLineage · KQL alerts · runbooks
Source DB SQL / NoSQL Operational CDC Capture Debezium / Native Capture Event Hub / Pub-Sub Partitioned, replayable Transport Dataflow / ADF Stream + micro-batch Databricks Curate · Enrich · Dedup Transform BigQuery Synapse · Fabric Power BI KQL Alerts Serve low-impact reads change events near real-time heavy enrichment query-ready consumer surface

CDC · Event Hub / Pub-Sub · Dataflow · ADF · Databricks · BigQuery · Synapse · Power BI · KQL

Pattern distilled from production delivery at Ashley and prior enterprise programs. Tooling adapts to the cloud in play; the contract (capture → transform → serve → observe) stays the same.

Experience

Delivery timeline with outcomes

Total experience: -- since June 2022.

Dec 2024 – Present

Data Engineer · Ashley Global Capability Center

  • Designed and led adoption of the org's first AI-integrated BigQuery SDLC pattern — repo-first operating model with AI guardrails and human-gated deployment. Cut representative delivery from ~5 days to 1–2 days and became the reference other Ashley teams follow. See the initiative →
  • Spearheaded GCP data platforms with BigQuery, Dataflow, and Cloud Functions for 15TB+ workloads.
  • Implemented CDC and integration frameworks across heterogeneous systems with .NET Function Apps and APIs.
  • Introduced AI-assisted workflows that contributed to 80% productivity improvement in delivery cycles.
Jul 2023 – Dec 2024

Data Engineer · Ashvad Tech Private Limited

  • Built scalable ETL pipelines using Azure Data Factory and Synapse for enterprise reporting and analytics.
  • Delivered .NET Web API integration services and Hangfire-based orchestration for automated workflows.
  • Supported migration to Entra-only authentication and strengthened data access governance.
Jul 2022 – Jul 2023

Data Engineer · Provana

  • Developed automated ETL frameworks in ADF and Synapse for repeatable onboarding and operations.
  • Configured Logic Apps and KQL-driven monitoring patterns for proactive incident handling.
  • Improved operational throughput through workflow standardization and automation.

Personal repos

Sandbox & side work

A selection of my personal GitHub repos — sandboxes where I prototype patterns and explore tools. Employer work is covered in Experience and the Signature Initiative.

Analytics Pipeline

TripAdvisor Reviews Analysis

Problem: Build robust sentiment analytics with business-ready reporting outputs.

Built: End-to-end Azure analytics pipeline using Synapse, Data Lake, Data Warehouse, Power BI, and Azure ML.

Result: Personal sandbox — produced a working end-to-end pattern (ingest → warehouse → ML → Power BI) on sample TripAdvisor data; useful as a reference layout, not a production deployment.

Synapse · Data Lake · Data Warehouse · Power BI · Azure ML

Open Repository

Near Real-Time Signals

Azure Real-Time Twitter Data

Problem: Capture social signals and process them continuously for near real-time analysis.

Built: Streaming workflow with Azure Functions, Event Hub, and Stream Analytics orchestrated through Python.

Result: Personal sandbox — a runnable proof of an Azure streaming wiring (Function → Event Hub → Stream Analytics) built to learn the moving parts, not to operate continuously.

Python · Azure Functions · Event Hub · Stream Analytics

Open Repository

Cloud-Native API

Cosmos DB Books Library API

Problem: Demonstrate a practical REST API backed by a globally distributed NoSQL store with clean data-access patterns.

Built: Python service exposing CRUD endpoints over an Azure Cosmos DB container with environment-driven configuration.

Result: Personal sandbox — a compact, deployable reference for Cosmos-backed CRUD and document modelling; intended as a starting template, not a live service.

Python · REST · Azure Cosmos DB

Open Repository

Now

Currently building & learning

Building

CI/CD on PR merge for the BigQuery SDLC — closing the last manual deployment gap while keeping the human PR gate.

Building

Generated change-script tooling that diffs the repo against a target BigQuery project and emits an auditable migration plan.

Learning

Microsoft Fabric Real-Time Intelligence — eventstreams, KQL DB, and Activator patterns for low-latency analytics.

Week 1

What I'd build for your team in week 1

  • Audit one critical pipeline for observability gaps — lineage, freshness SLOs, KQL alert coverage — and ship a one-page risk note.
  • Stand up a CDC POC on a non-prod source → event hub → curated table, with replay and idempotent merge proven end-to-end.
  • Document one repo-first standard for AI-assisted change (naming, audit columns, PR gate) so the next 10 changes follow the same shape.

Concrete, low-blast-radius, designed to surface what to invest in next.

Credentials & contact

Let us build something meaningful

Awards

Dirty Fingernail Award · 2025 Above & Beyond · Q4 2025

Internal recognition for hands-on, deep-dive problem solving on complex production issues.

Certifications

Microsoft Azure Data Scientist Associate · Recert Nov 2025 DeepLearning.AI TensorFlow Developer

Open to

Data Engineer · Cloud Data Platform Engineer · Chennai, India · remote & hybrid welcome

If your team is building data platforms, integrating cloud ecosystems, or modernizing delivery with reliable AI-assisted engineering, I am open to collaborating.

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