Back
Tier-1 brand, mid-level generalist role, metro context and broad skill requirements increase competition.
Data engineering skills are broadly transferable, though life‑sciences and Databricks experience increase fit sensitivity.
Multiple mandatory cloud/Databricks skills and explicit 5+ years requirement create strict shortlisting filters.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Develop and maintain ETL/ELT pipelines for ingesting data into a data warehouse, ensuring performance and scalability.
Own end-to-end data engineering processes including data quality, security protocols, and infrastructure optimization.
Collaborate with cross-functional data teams and support adoption of data and technology strategies in an Agile/Product based environment.
3-5 years of hands-on data engineering experience, preferably with cloud environments (AWS preferred).
Expertise in Databricks, AWS services including Glue, Lambda, S3, Redshift, Athena, CloudFormation, and AWS API development.
Strong programming skills in Python, PySpark, Scala, R, or related languages and experience with SQL and database technologies.
Experience in Agile/Product based teams and ability to work with minimal oversight in a fast-paced environment.
Experienced in full life cycle data management including data lakehouses, master data management, and analytics/AI ML capabilities.
Comfortable working in global, cross-functional teams and implementing product-based data solutions.
Strong operational ownership mindset with focus on process improvement and delivering complex data infrastructure solutions.