Back
Strong employer brand, metro location, and senior Databricks specialization yield moderate candidate competition.
Core data engineering skills transfer across industries, though financial domain experience increases hiring relevance.
Explicit 14–18 years and mandatory Databricks/Spark/AWS plus programming requirements make filters highly stringent.
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 workflows using Databricks and Apache Spark.
Optimize ETL/ELT pipelines for batch and near real-time processing ensuring data quality, governance, and operational reliability.
Collaborate with architects and business stakeholders to translate requirements into technical implementations and support production deployments.
14-18 years hands-on experience with Databricks and Apache Spark (Python/Scala).
Strong experience in Data Engineering and Big Data technologies with cloud platforms such as AWS or Azure.
Proficiency in SQL, Python/Scala programming, data pipeline design, ETL/ELT frameworks, and data modeling.
Work Experience Required: 14-18 years relevant experience
Experienced in building scalable and production-grade data platforms with a focus on performance optimization and cost efficiency.
Comfortable working in agile, global distributed teams collaborating across technical and business stakeholders.
Strong problem-solving skills demonstrated by ownership of end-to-end data engineering solutions leveraging modern cloud technologies.