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Tier-1 brand, metro location, and mid-level generalist data engineer role drive high competition.
Core data engineering skills are transferable, but Snowflake/DBT requirements increase domain specificity.
Explicit 5–8 years plus mandatory Snowflake, DBT, Airflow, SQL and Python increases shortlisting strictness.
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Design, build, and launch scalable production data models and pipelines across the enterprise data lifecycle (ingestion to consumption).
Own complex end-to-end system design decisions, including architectural tradeoffs and documentation.
Implement data governance and quality frameworks; align with product roadmaps and lead quality assurance and stakeholder engagement activities.
Bachelor's degree in Computer Science, Information Systems, or related field or equivalent practical experience.
5–8 years of hands-on experience building and operating production data pipelines, data models, and platform infrastructure at scale.
Expert-level SQL and strong Python skills; hands-on experience with Snowflake (including Snowpark and related features), DBT, and Apache Airflow.
Work Experience Required: 5–8 years relevant experience; Notice Period: Not explicitly mentioned in the JD.
Deep expertise in system design and architectural reasoning, able to make and communicate technical decisions clearly across technical and non-technical stakeholders.
Experience operating at the intersection of data engineering, platform thinking, and stakeholder alignment in dynamic environments.
Strong ownership instincts with capability to manage ambiguous requirements and lead Agile data product development practices.