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About The Opportunity A quick-scaling technology services firm operating in the Data Engineering and Cloud Analytics space, we partner with enterprises to build scalable data pipelines, automate ETL workflows, and unlock real-time insights from structured and unstructured datasets. Our Python PySpark developers architect and deploy high-performance data solutions on cloud platformsenabling faster decision-making, predictive modeling, and regulatory compliance for global clients. Role & Responsibilities Design, develop, and optimize PySpark ETL pipelines for large-scale batch and streaming data processing. Collaborate with data scientists and analysts to ingest, transform, and model data for ML training and reporting use cases. Implement data quality checks, error handling, and logging frameworks within Spark jobs for production-grade reliability. Integrate PySpark workflows with cloud data lakes (AWS S3, Azure Data Lake), metastores (Hive, Glue), and orchestration tools (Airflow, Luigi). Performance-tune Spark applicationspartitioning, caching, broadcasting, and resource allocationto reduce job runtimes and costs. Write clean, modular, testable Python code with unit/integration tests and document architecture decisions for team scalability. Skills & Qualifications Must-Have PySpark Python Apache Spark ETL Development SQL Data Lake Architecture AWS S3 Apache Airflow Preferred Azure Data Lake Databricks Delta Lake Perks & Culture Highlights On-site collaborative workspace in major Indian tech hubs with modern infrastructure and R&D; labs. Opportunities to upskill in cloud-native data platforms and GenAI-powered data pipelines. Fast-track growth opportunities with cross-functional exposure to data science, ML engineering, and cloud architecture teams. Skills: data,python,lake,spark,cloud .
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The employers posting the most developer roles are Link Group (369 jobs), Upvanta (257), jobgether (226), and OfferZen (212). If you're applying to any of these, research their hiring patterns. They move fast and post often, which means they're either scaling hard or replacing people who didn't fit.
In your interview, expect this: 'Walk me through the last time you had to debug something that took you more than an hour. What was it, what did you try first, and what would you do differently?' Hiring managers ask this to see if you think systematically or just try random fixes. Have a real example ready with specifics.