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Tier-1 brand plus a mid-level, generalist data engineer profile creates high applicant competition.
Data engineering skills (ETL, SQL, Spark, cloud) are broadly transferable across industries, so sensitivity is low.
Explicit 4–5+ years requirement and specific data stack needs (Spark, Snowflake, Python, SQL) make screening moderately strict.
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Design and develop scalable data engineering solutions and ETL/data pipelines to support AI, ML, and advanced analytics initiatives globally.
Collaborate with cross-functional business and product teams to translate requirements into technical solutions and optimize data performance.
Ensure compliance with data quality, security, and governance standards while contributing to community best practices and documentation.
Bachelor’s Degree or equivalent in Computer Science, Engineering, or relevant field.
4 to 5+ years of experience in data engineering or related roles using tools like Spark/Scala, Informatica/IICS/Dbt.
Proficiency in SQL, relational databases, scripting languages (Python, Shell), and cloud-based data platforms such as Snowflake.
Work Experience Required: 4 to 5+ years in data engineering or comparable roles.
Experienced working in fast-paced, cross-functional agile teams managing complex data architecture and engineering problems.
Strong technical problem solver with the ability to learn new data and software engineering technologies quickly and deliver pragmatic solutions.
Comfortable operating with leading-edge technologies and committed to high-quality standards including automated testing, CI/CD, and production-level code reviews.