// Senior Data Engineer & AI Engineer · 5 YOE

Onkar
Antad

Building large-scale data platforms on GCP & Azure Databricks. Architecting event-driven pipelines, Lakehouse systems, and GenAI / Agentic AI applications using LangChain & LangGraph.

5
Years Experience
5
Cloud Certifications
1M+
Records / Day
99.9%
Pipeline Uptime
LinkedIn GitLab Email

// architecture

System Designs

GCP Data Platform Architecture Event-driven ETL/ELT pipeline on Google Cloud Platform with Cloud Run, Eventarc, Workflows, and BigQuery ingestion orchestration processing serving Source APIs REST / GraphQL GCS Bucket Raw files Pub/Sub Event stream Eventarc Triggers Cloud Workflows Orchestration Cloud Build CI/CD + Terraform Cloud Logging Monitoring / Alerts Cloud Run ETL / ELT jobs Dataflow Streaming ETL Dataproc Spark batch jobs BigQuery Partitioned + Clustered 25% cost reduction Looker / BI Dashboards SonarQube Code quality Python ETL Reusable framework Terraform IaC Env setup: 2-3d → 3h ✦ 99.9% uptime · 1M+ records/day
Lakehouse Architecture on Azure Databricks Bronze-Silver-Gold Medallion architecture on Azure Databricks with PySpark, ADF, and Delta Lake medallion architecture — azure databricks Azure Data Factory (ADF) BRONZE Raw ingestion Raw files (ADLS) Schema validation Delta Lake write Autoloader (CDC) SILVER Cleaned & conformed PySpark transforms SCD Type 2 logic Data quality rules GOLD Business-ready Star schema models Fact + Dim tables Aggregations / KPIs BigQuery export Analytics BI / Reporting Data Science 50% less manual intervention · Spark perf optimized
Kafka CDC Pipeline Architecture Real-time CDC pipeline using Kafka reducing data latency from 4-6 hours to under 15 minutes kafka cdc — real-time change data capture SQL Server Source DB Debezium CDC connector Kafka Topics INSERT / UPDATE / DELETE Spark Structured Streaming Delta Lake ACID transactions BigQuery Analytics layer BEFORE 4–6 hours latency batch processing → AFTER <15 min latency real-time CDC pipeline
GenAI and Agentic AI Stack LangChain and LangGraph agentic AI architecture with vector databases and LLM integration genai + agentic ai — langchain / langgraph User Query LangGraph Agent Orchestrator LangChain tools / skills BigQuery Tool GCS Loader Tool Python Executor Qdrant Vector DB (RAG) LLM GPT-4 / Claude Prompt engineering Memory Store Conversation context Response Agentic AI · RAG Pipelines · Multi-tool Orchestration · LangGraph State Machines · Prompt Engineering

// experience

Where I've Built

OCT 2024 — PRESENT · PUNE, INDIA
Accenture
Senior Data Engineer · Fortune 500 Automotive Client
  • Architecting GCP data platforms covering pipeline design, BigQuery modelling, CI/CD and monitoring for multiple business teams.
  • Built event-driven ETL/ELT pipelines using Cloud Run, Workflows, Eventarc and BigQuery processing over 1M records daily at 99.9% uptime.
  • Tuned BigQuery tables with partitioning and clustering, reducing compute costs by ~25% and query times significantly.
  • Created a reusable Python ETL framework + GitHub standards, cutting new pipeline build time by nearly half.
  • Automated environment setup using Terraform IaC: 2–3 day setup → under 3 hours.
  • Introduced SonarQube, FOSSA and Pytest into the dev workflow for early issue detection and code quality improvement.
MAY 2021 — OCT 2024 · BANGALORE, INDIA
Sapiens
Data Engineer · Insurance Technology Platform
  • Built the full Bronze-Silver-Gold Lakehouse on Azure Databricks using PySpark and ADF, reducing manual pipeline intervention by 50%.
  • Engineered Kafka-based CDC pipelines bringing data latency from 4–6 hours → under 15 minutes.
  • Spark performance tuning via partition optimisation, caching and join strategies — improving job performance while reducing cluster costs.
  • Designed SCD Type 2 data models with Star Schema (Fact and Dimension tables) for historical tracking and clean analytics reporting.
  • Migrated SQL Server logic to BigQuery as part of a cloud modernisation effort.
  • Built GraphQL and REST API microservices on Spring Boot with Redis caching, improving API response times by ~40%.

// skills

Technical Stack

☁
Cloud & Data Platforms
GCP BigQuery Dataproc Dataflow Cloud Run Eventarc GCS Azure Databricks ADF
⚙
Data Engineering
Apache Spark PySpark Delta Lake Apache Iceberg Kafka CDC Apache Airflow DBT Medallion Arch SCD Type 2
🤖
AI & GenAI
LangChain LangGraph Agentic AI LLMs Qdrant Vector DBs Prompt Engineering RAG Pipelines
🛠
Programming & DevOps
Python SQL Java Terraform CI/CD GitHub Actions Docker SonarQube Pytest
⚡
Optimization & Architecture
BQ Partitioning BQ Clustering Spark Tuning Lakehouse Dimensional Modeling IaC
🔌
APIs & Integration
REST API GraphQL Spring Boot Redis Trino Talend SQL Server

// certifications

5 Cloud Certifications

Professional Cloud Architect
Google Cloud · Feb 2026 – Feb 2028
Professional Data Engineer
Google Cloud · Feb 2026 – Feb 2028
Associate Cloud Engineer
Google Cloud · Apr 2025 – Apr 2028
Data Engineer Professional
Databricks · Feb 2026 – Feb 2028
Data Engineer Associate
Databricks · Nov 2025 – Nov 2027

// education

Academic Background

SEP 2020 – APR 2021
PG-DAC in Advanced Computing
CDAC ACTS, Pune
82%
MAR 2016 – MAR 2020
B.E. Mechanical Engineering
Walchand Institute of Technology, Solapur
74%

// contact

Let's Connect

Email
onkarantad@outlook.com
LinkedIn
linkedin.com/in/onkarantad
GitLab
gitlab.com/straw-hat-crew
Location
Pune, India