Source description
About the role
Lead data engineering and architecture projects independently and ensure timely delivery.
Design, build, and maintain resilient and scalable data pipelines.
Implement and manage ETL and ELT processes for efficient data ingestion and transformation.
Apply performance-tuning techniques across data processing workflows.
Design and support real-time streaming data architectures where applicable.
Build reports and data visualizations using Tableau, Power BI, or similar BI tools.
Own data problems end-to-end, including data collection, extraction, and data cleansing.
Contribute to and implement maintenance strategies for datasets to ensure reliability and quality.
Maintain strong data organization practices across platforms and pipelines.
10+ years of hands-on experience working as a Data Engineer, BI Engineer, or Data Warehousing Engineer.
Strong experience with ETL tools such as Talend, SSIS, Informatica, and similar platforms.
Expertise in Google Cloud Platform services including Cloud Storage (Buckets), Cloud Data Fusion, BigQuery, and Google Analytics.
Strong experience with AWS and GCP technologies including S3, SQS, Redshift, Kinesis, GCP Buckets, Cloud Data Fusion, and BigQuery.
Working knowledge of Java and Python along with associated build and packaging tools.
Strong hands-on experience with Python for data processing and automation.
Experience with NoSQL databases such as MongoDB, DynamoDB, Druid, Hive, and Presto.
Experience with AWS services including S3, SQS, Redshift, and DocumentDB.
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