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The World of BookMyShowLaunched in 2007, BookMyShow, owned and operated by Big Tree Entertainment Pvt. Ltd. (founded in 1999), is Indias 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 U2s The Joshua Tree Tour, NBAs debut games in India, Disneys 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, Indias largest home-grown transactional video-on-demand (TVOD) platform. Role OverviewWere looking for a Data Enthusiast who combines strong data engineering fundamentals with hands-on experience applying machine learning in production. Youll 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 ProfileDesign, 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 reproducibilityCollaborate with Data Scientists/ML Engineers to deploy models (batch and/or real-time), including via Databricks Model Serving or ML flowOptimize Spark jobs, SQL warehouses, and cluster configurations for cost and performanceBuild 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 maskingPartner with platform/infra teams on job orchestration, CI/CD for data & ML pipelines, and cost optimizationContribute 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 stakeholdersEvaluate 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)Your Checklist1-3 years of data engineering experience working on Databricks in productionStrong proficiency in PySpark/Spark SQL and PythonSolid 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 ServingStrong 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 dont need to be building models yourself, but you should understand what good looks like)Preferred SkillsExperience 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 workloadsBackground with experimentation platforms or A/B testing infrastructureFamiliarity with orchestration tools (Databricks Jobs, Airflow) and CI/CD for data pipelines .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.