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Purpose/Objective The Databricks Engineer will build, test, optimise and support Databricks-based data pipelines and Lakehouse components. The role requires strong hands-on Python coding capability, PySpark development skills and practical experience in production-grade data engineering. Key Responsibilities of Role Key Responsibilities - Develop data pipelines using Python, PySpark, SQL and Databricks notebooks/jobs. Build ETL/ELT workflows for ingestion, transformation, validation and publishing of data. Implement Delta Lake tables, partitioning strategies and optimised query patterns. Perform unit testing, data reconciliation, exception handling and production issue support. Work with senior engineers and architects to implement standardised Lakehouse patterns. Participate in code reviews, sprint delivery, release activities and documentation. Support CI/CD integration, Git-based development and deployment practices. Monitor pipeline failures, troubleshoot performance issues and support operational runbooks. Mandatory Skills - Python coding is mandatory: candidate must clear a hands-on Python coding round. Strong Python programming knowledge including functions, classes, exception handling and file processing. Hands-on PySpark development experience. Databricks notebooks, jobs/workflows and cluster usage experience. Good SQL skills and understanding of joins, aggregations, window functions and query optimisation. Experience with ETL/ELT, data transformation and data validation logic. Basic understanding of Git and CI/CD practices. Python Coding Assessment Scope - Data transformation using lists, dictionaries, files or dataframes. Writing reusable functions and clean modular code. Handling missing, duplicate or invalid data records. Basic object-oriented programming concepts. Exception handling and logging approach. Simple API/file ingestion or parsing problem. Optional PySpark coding problem for O3 candidates. Preferred Skills - Delta Lake, Unity Catalog, Delta Live Tables or structured streaming exposure. Cloud platform experience on Azure or Google Cloud Platform. Data quality, metadata, lineage and governance awareness. Terraform, Databricks Asset Bundles or deployment automation exposure. Databricks Associate certification will be an advantage. Selection Criteria - Python coding assessment: mandatory and eliminatory. Technical interview covering Databricks, PySpark, SQL and production data engineering. Scenario discussion on pipeline failure, performance tuning and data quality issue handling. Final discussion on communication, ownership, learning ability and team fit. Technical Competencies Data Engineering & ETL Development,Databricks Lakehouse & Delta Lake Architecture,Data Quality, Testing & Production Support,CI/CD, DevOps & Operational Excellence Qualifications and Experience 2-3 years overall experience, with hands-on Python, PySpark and Databricks delivery exposure .
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.