Software Development Engineer - I Data

The World Of BookMyShow

Launched in 2007, BookMyShow, owned and operated by Big Tree Entertainment Pvt. Ltd. (founded in 1999), is India's leading entertainment destination with global operations and the one-stop shop for every entertainment need. The firm is present in over 650 towns and cities in India and works with partners across the industry to provide unmatched entertainment experiences to millions of customers.

Over the years, the company has evolved from a purely online ticketing platform for movies across 6,000 plus screens, to end-to-end management of live entertainment events including music concerts, live performances, theatricals, sports and more. Some of the key properties that BookMyShow has brought to its markets include U2's The Joshua Tree Tour, NBA's debut games in India, Disney's Aladdin, Cirque du Soleil BAZZAR as well as international artists such as Coldplay, Ed Sheeran, and Justin Bieber. BookMyShow is invested in providing the best user experience, whether on-ground or online.

The company has developed 'BookMyShow Stream', India's largest home-grown transactional video-on-demand (TVOD) platform.

Role Overview

We're looking for a Data Enthusiast who combines strong data engineering fundamentals with hands-on experience applying machine learning in production. You'll build and scale data pipelines on Databricks, and partner closely with Data Science/Business Intelligence teams to operationalize models - from feature engineering through deployment and monitoring as well as generating insights that create business impact

Your Profile

• Design, build, and maintain scalable ETL/ELT pipelines using Databricks (Spark, Delta Lake, Lakeflow/DLT, Unity Catalog)
• Develop and productionize feature pipelines for ML use cases, ensuring reliability, freshness, and reproducibility
• Collaborate with Data Scientists/ML Engineers to deploy models (batch and/or real-time), including via Databricks Model Serving or ML flow
• Optimize Spark jobs, SQL warehouses, and cluster configurations for cost and performance
• Build and maintain system-level observability for pipelines and ML jobs (usage, cost, quality, drift)
• Implement data quality checks, testing, and monitoring across the medallion architecture (bronze/silver/gold)
• Own Unity Catalog governance for datasets and features — access controls, lineage, and PII masking
• Partner with platform/infra teams on job orchestration, CI/CD for data & ML pipelines, and cost optimization
• Contribute to architecture decisions around lakehouse design, streaming vs. batch tradeoffs, and tool selection (build vs. buy)
• Perform analysis on top of the data you build, answer ad-hoc business questions, validate metrics, and spot data quality issues before they reach stakeholders
• Evaluate and apply LLMs/agentic frameworks responsibly within the team, balancing accuracy, cost, and governance (e.g., row/column-level access control on what an AI agent can query)
  
  

• 1-3 years of data engineering experience working on Databricks in production
• Strong proficiency in PySpark/Spark SQL and Python
• Solid understanding of Delta Lake, Unity Catalog, Lakeflow/DLT, and Databricks system tables (billing, compute, query history) Experience building and maintaining feature pipelines or ML data infrastructure (feature stores, training/serving data parity)
• Familiarity with MLflow (experiment tracking, model registry) and/or Databricks Model Serving
• Strong SQL skills and experience with warehouse performance tuning (query optimization, materialization strategies, cluster sizing)
• Understanding of ML fundamentals — enough to have real conversations with Data Scientists about features, drift, and model lifecycle (you don't need to be building models yourself, but you should understand what "good" looks like)
  
  

• Experience with real-time/streaming architectures (Structured Streaming, Kafka, Lakebase or similar)
• Exposure to LLM/agentic tooling (Databricks Genie, RAG pipelines, vector search)
• Experience with cost governance/FinOps for Databricks workloads
• Background with experimentation platforms or A/B testing infrastructure
• Familiarity with orchestration tools (Databricks Jobs, Airflow) and CI/CD for data pipelines