Bijil Subhash

Forward Deployed Data Engineer

• Grew a key strategic account to $2M AUD in 8 months, embedded client-side to architect, build, and productionise end-to-end solutions on a native GCP stack (BigQuery, Cloud Storage, Cloud Run) while shaping the account roadmap.
• Lead the enterprise data platform build for a geopolitical risk advisory firm — state-aware batch and streaming ingestion, an asset-driven orchestration layer, and a modular transformation framework with end-to-end monitoring and alerting.
• Designed and built a state-driven micro-payments platform for advisor remuneration, retiring a 15-year-old manual process, removing a compliance exposure, and saving 100+ hours annually.
• Implemented event-driven integrations combining a Pub/Sub queue and webhook listeners for real-time propagation with Dagster-orchestrated batch reconciliation to catch drift.
• Designed the streaming ingestion architecture behind a business-critical AI product, and a near-real-time pipeline transforming live CMS updates into RAG-ready output.
• Led a 6-week migration from Salesforce to monday.com, redesigning the CRM object model to modelling best practice and phasing the cutover to a single source of truth.
• Partnered with C-suite, GTM, and analyst stakeholders to define OKR metrics, saving 200+ hours annually and surfacing $500k+/year in uncaptured revenue.

Partner Instructor & Data Engineer

• Delivered multi-day Snowflake and dbt training to 100+ engineers and analytics practitioners, running hands-on labs for enterprise cohorts of up to 25.
• Took teams from fundamentals to production practice: incremental models, snapshots, macros, testing, CI/CD, query optimisation, and advanced SQL.
• Spearheaded the AI engineering curriculum, teaching teams to build pipelines and semantic models using Claude Code, Snowflake Cortex, dbt Wizard, agent harnesses, and MCP.
• Built the IaC framework provisioning training environments end to end — dbt Cloud accounts and projects alongside Snowflake schemas, roles, and compute via Terraform.

Founding Data Engineer

• Developed a config-driven Python ingestion framework covering 150+ API endpoints with dlt, cutting ingestion costs by ~95% and migrating a client off low/no-code tooling.
• Migrated a client from unorchestrated AWS Lambda pipelines to a governed, modular platform on MotherDuck, Dagster, and dbt, establishing observability and deployment practice from day one.
• Built an end-to-end platform on MotherDuck and DuckDB for a seed-stage startup — ingestion (dlt), transformation (dbt), orchestration, and infrastructure (Terraform) on GCP.
• Built an Azure Databricks platform applying modelling, transformation, DataOps, and IaC across Python, dbt, and Terraform to migrate a client off Snowflake.
• Designed a Unity Catalog–enabled Databricks architecture entirely through IaC, managing 3 workspaces from a central config-driven Terraform framework.
• Delivered LLM and analytics workloads on GCP (BigQuery, Cloud Run, Cloud Functions, Looker Studio) for a mid-tier organisation.

Data Engineer

• Founding data engineer at a Series A startup, owning the entire platform end to end — ingestion, transformation, orchestration, analytics, and governance — built from scratch.
• Designed an event-driven architecture on GCP (Pub/Sub, Cloud Run, Cloud SQL, BigQuery) ingesting ~300M rows daily to power a virtual power plant monitoring distributed renewable assets in real time.
• Built a production Python ingestion framework for time-series telemetry from residential IoT devices and CRM data via REST APIs.
• Deployed a hybrid Dagster instance as the single orchestration layer, using declarative automation, asset checks, and factory patterns.
• Established the dbt project and modelling standards, applying software engineering practice to analytics transformation.
• Rolled out Terraform IaC patterns, CI/CD for platform deployments, and a test suite covering core service functionality.

Associate → Senior Data Engineer

• Developed a Kubernetes operator framework surfacing real-time data product status for one of Australia’s Big 4 banks, a core component of their enterprise data mesh (GKE, Pub/Sub, Cloud Run, Python, Terraform, Docker).
• Blueprinted the migration of legacy AWS Databricks workspaces to Unity Catalog for Australia’s largest energy retailer, building config-driven Terraform patterns and reusable Azure DevOps pipelines.
• Contributed 800+ dbt models on a national retail chain’s Teradata-to-BigQuery migration, applying the analytics engineering lifecycle at enterprise scale.
• Built a consolidated data model for a vulnerability management solution using dbt and Cloud Composer, merging 3 sources (~2B rows) for organisation-wide risk assessment.
• Implemented a GenAI architecture on Vertex AI, dbt, and Cloud Composer, turning ~20,000 free-text survey responses into structured insight for leadership.
• Designed executive dashboards for delivery metrics in Looker and Looker Studio, saving operational leadership 20+ hours weekly.

Databricks, Snowflake, BigQuery, MotherDuck / DuckDB, GCP, Azure, AWS

dlt, Fivetran, Polytomic, Pub/Sub, custom Python frameworks, CDC & streaming

dbt, PySpark, Spark SQL, dimensional modelling, semantic layer

Dagster, Airflow

Terraform, Docker, Kubernetes (GKE), CI/CD (GitHub, GitLab, Azure DevOps, Cloud Build), Pytest

Vertex AI, Snowflake Cortex, Claude Code, MCP, RAG, Looker, Looker Studio, Power BI, Omni

Unity Catalog, Cloud IAM

Python, SQL

PhD, Chemical Engineering

Bachelor of Chemical Engineering