Strong experience in AWS Data Engineering: S3, Redshift, EMR, Glue, Lambda
Hands-on expertise in Spark, PySpark, Spark SQL
Experience with Hadoop, Hive, Kafka
Strong proficiency in Python, SQL, and preferably Scala
Experience building ETL/ELT pipelines and large-scale data platforms
Strong knowledge of Data Warehousing, Data Modeling (Star/Snowflake Schema
Experience with Apache Airflow or similar orchestration tools
Exposure to Databricks, Snowflake, Delta Lake is a plus
Experience in performance tuning, data quality, and production support
Ability to lead technical discussions, mentor developers, and collaborate with stakeholders

Architect and maintain scalable ETL/ELT pipelines to ingest, process, and transform massive datasets for downstream analytical consumption.
Design sophisticated data models that ensure data integrity, performance, and ease of access for business intelligence reporting.
Optimize data warehouse performance by fine-tuning storage strategies and query execution plans to support high-concurrency environments.
Lead the migration and integration of data workloads onto AWS cloud infrastructure to improve system elasticity and cost-efficiency.
Implement real-time data streaming solutions using Kafka to enable low-latency data availability for critical business applications.
Mentor junior engineers and conduct code reviews to enforce best practices in software engineering and data quality standards.

Lead Data Engineer - Python/SQL/ETL

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