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Job Description
Structured overview of role & requirementsAbout This Role
Own architecture, design, and growth of data products and scalable data pipelines using Scala, Python, Spark within a Lakehouse data platform environment.
Develop and maintain ETL pipelines in cloud (AWS) and hybrid big data environments ensuring SLAs and operational efficiency.
Collaborate with Data Product Managers, Architects, and Engineers to deliver impactful data solutions supporting business insights and data governance.
Minimum Requirements
Minimum 5 years of data engineering experience building large-scale data pipelines.
Strong hands-on experience with AWS (S3, EMR, EC2), Databricks, Spark, Python, Scala, and SQL.
Bachelor's degree in Computer Science, Information Systems, Software, Electrical/Electronics Engineering or equivalent experience.
Work Experience Required: At least 5 years in data engineering.
Ideal Candidate Profile
Experienced with large scale distributed data systems and cloud-native data platforms, specifically AWS and Databricks environments.
Proficient in designing and implementing data pipelines in Scala, Python and with tools like Airflow, Spark, and Snowflake.
Comfortable working cross-functionally with product teams to build data-driven products and capable of optimizing for operational efficiency and cost.