Back
Senior niche AWS/PySpark data engineering lead reduces applicant pool despite Mumbai location.
Core AWS, Spark, and data-engineering skills are easily transferable across industries.
Multiple mandatory AWS, PySpark, SQL, and IaC requirements enforce strict technical filtering.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead design and development of cloud-native data lakes and warehouses using AWS services including S3, Glue, Athena, and Redshift.
Build and optimize scalable, fault-tolerant batch and streaming data pipelines using Spark/PySpark and Python for large-scale datasets.
Own solution quality, performance, and production stability while collaborating with stakeholders and managing CI/CD pipelines and infrastructure as code.
Strong hands-on experience with AWS data engineering tools: S3, Glue, Athena, Redshift.
Proficient in SQL and Python with expertise in complex transformations, performance tuning, and handling large datasets.
Experience with Spark/PySpark for ETL development and performance optimization.
Bachelor’s or master’s degree in Computer Science, Information Systems, Data Engineering, or related field.
Experienced technical lead with a strong ownership mindset for production stability and solution quality in cloud data platforms.
Skilled in building scalable distributed data processing pipelines and infrastructure automation using Terraform or CloudFormation.
Familiar with Agile delivery in global teams and holds AWS certifications like Data Analytics or Solutions Architect.