Back
Tier-1 brand, mid-level generalist role, metro location, and broad Databricks/PySpark skillset drive high competition.
Data engineering skills are transferable but require domain tooling and governance knowledge, yielding medium fit sensitivity.
Explicit 5–8 years requirement plus mandatory Databricks/PySpark and ETL skills enforce high shortlisting strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, develop, and maintain scalable data pipelines and ETL/ELT processes using big data technologies like Databricks, Apache Spark (PySpark, SparkSQL) to support large-scale data processing and business analytics.
Take full ownership of data pipeline projects from inception to deployment, managing scope, timelines, and risks, while ensuring data quality and security.
Collaborate with cross-functional and global teams including Data Architects, Data Scientists, and business SMEs to deliver data solutions aligned with business needs across geographic regions.
Bachelor’s or Master’s degree in Computer Science, IT, or related field.
5 to 8 years of relevant experience working with big data platforms and tools, including Databricks and Apache Spark.
Hands-on expertise in building and optimizing ETL pipelines, data analysis using SQL, and data governance frameworks.
Willingness to work later shifts including evening or night as per business requirements.
Experienced data engineer comfortable managing end-to-end data pipeline projects with ownership beyond coding, including risk and timeline management.
Proficient in optimizing big data jobs with performance tuning techniques such as Spark job tuning, caching, partitioning, and indexing for scalability.
Capable of working in distributed, cross-time-zone teams communicating effectively to maintain alignment and deliverables.