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Common senior data engineer title, metro Bangalore location, and mid-level experience increase applicant competition.
Data engineering skills are transferable, but AWS Glue and QuickSight requirements increase domain specificity.
Explicit 5–10 years plus mandatory AWS Glue, PySpark, and QuickSight skills make filters strict.
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Own end-to-end BI delivery including building and maintaining ETL pipelines on AWS Glue (PySpark) and designing interactive dashboards in Amazon QuickSight.
Develop and optimize data pipelines ingesting from multiple SaaS sources, ensuring data quality, incremental loads, and pipeline orchestration via AWS Step Functions.
Translate business requirements into reliable data products for operational and strategic decision-making, maintaining clear technical documentation and collaborating asynchronously across time zones.
5–10+ years of professional experience in data engineering, BI development, or analytics engineering.
Strong hands-on experience with AWS Glue, AWS Step Functions, Amazon S3, Athena, and IAM.
Proficiency in Python, PySpark, and SQL with experience building ETL pipelines from REST APIs or SaaS platforms like Salesforce, JIRA, Zendesk.
Experience with Amazon QuickSight including dataset creation, dashboard publishing, and knowledge of columnar/lakehouse table formats such as Apache Iceberg.
Experienced working independently with minimal supervision, managing priorities and communicating asynchronously across time zones.
Highly skilled in designing and optimizing complex AWS data pipelines and BI dashboards for diverse operational and executive stakeholders.
Comfortable handling both technical pipeline troubleshooting and translating business requirements into actionable data products within a modern cloud-native environment.