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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.
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.