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Ajit Zod

@ajitzod

Full Stack Data Engineer building end-to-end data products Kafka ingestion to Databricks AI analytics to production dashboards.

India

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What I'm looking for

I’m looking for a role where I can own end-to-end data products—streaming ingestion, transformation, and AI-driven analytics on cloud data platforms—while building reusable frameworks and delivering dashboards that directly influence product and customer decisions.

I’m a Full Stack Data Engineer with 3+ years owning the complete data lifecycle—ingestion, transformation, AI-driven analytics, and delivery—at production scale. At BookMyShow, I independently shipped a Kafka-to-dashboard streaming analytics product, built a multi-gateway payment reconciliation system, and migrated production Spark workloads from Redshift to a Databricks Lakehouse (200+ jobs) using a reusable framework adopted across teams.

I thrive across the stack: Apache Kafka and REST/SFTP ingestion, PySpark and Delta Lake transformation, Databricks AI Functions and prediction modelling, and delivery via Streamlit/Power BI/Databricks Apps. I’ve also executed a 1B+ event CleverTap CDP migration with GCP Dataflow/Dataproc with zero data loss, and I bring Databricks Certified Professional Data Engineer (top 1% globally) plus Google Cloud Professional Data Engineer and ML Engineer credentials.

Experience

Work history, roles, and key accomplishments

Current

Data Engineer

Current

BookMyShow

Aug 2025 - Present (11 months)

Built an end-to-end CX agent-quality analytics product using Kafka ingestion, PySpark Structured Streaming, Databricks AI Functions for scoring, and a Streamlit dashboard for weekly CX/product decisions. Also delivered a daily payment-gateway reconciliation pipeline and migrated production Spark workloads from Amazon Redshift to a Databricks Lakehouse.

Data Engineer

Tredence Analytics

Jan 2023 - Aug 2025 (2 years 7 months)

Built PySpark ingestion and ELT transformation jobs using a Medallion Architecture on AWS S3 with Glue and Redshift/Athena as serving layers, automating most data onboarding from multiple sources. Also contributed to a supply-chain alert pipeline and optimized transformations to reduce query execution time and compute spend.