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Tier-1 brand, metro location, mid-level 3+ years, generalist data engineer skillset increases applicant competition.
Core data engineering skills (PySpark, SQL, ETL, pipelines) are highly transferable across industries.
Explicit 3+ years plus mandatory data engineering tech stack and platform experience increases shortlisting rigidity.
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Design, build, and maintain data ingestion, transformation, and egress pipelines handling large data volumes using Python, PySpark, SQL, and SSIS.
Collaborate with functional teams to understand and implement end-to-end data flows and integration requirements across multiple source systems.
Support production deployments including issue triaging, root cause analysis, and ensuring batch automation meets customer SLA compliance.
3+ years of experience in Data Architecture, Data Engineering, or related fields.
Strong hands-on experience with Python, PySpark, SQL, ETL processes, and cloud-based data platforms.
Experience with workflow management tools like Airflow and SSIS, and working knowledge of data formats (Parquet, JSON) and query frameworks (Hive, Presto).
Work Experience Required: Minimum 3 years; Notice Period: Not explicitly mentioned in the JD.
Experienced in building and supporting complex, large-scale data pipelines integrating diverse data sources such as Teradata, SAP ERP, SQL Server, Oracle, and APIs.
Capable of providing technical leadership and mentoring junior consultants with a focus on code quality and adherence to best practices.
Comfortable working in Agile environments with strong problem-solving skills and familiarity with version control tools like GitHub or Azure DevOps.