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Tier-1 brand, metro location, generic architect title, and broad common data-stack needs increase competition.
Core data engineering skills are widely transferable, though supply-chain planning experience raises domain preference.
Explicit 9+ years plus mandatory data architecture and specific tech stack make shortlisting highly stringent.
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Lead advanced data ingestion, transformation, validation, and publishing tasks ensuring performance tuning and scalable interface development.
Build and manage large-volume data pipelines integrating diverse data sources such as Teradata, SAP ERP, SQL Server using API or batch methods.
Provide technical leadership by reviewing code, guiding junior consultants, and supporting production deployment and issue resolution to meet customer SLAs.
9+ years of relevant experience including 5+ years in Data Architecture, Data Engineering or related fields.
Strong proficiency in Python, PySpark, SQL, with experience in SSIS, Airflow, and working knowledge of relational databases and query frameworks.
Experience integrating various data sources including Teradata, SAP ERP, SQL Server, Oracle, Sybase, and working with Parquet, JSON, Restful APIs, HDFS, Delta Lake.
Familiarity with agile methodologies and version control platforms like GitHub or Azure DevOps.
Experienced in complex data pipeline design and large-scale data processing within enterprise environments, especially for supply chain planning applications.
Technical leadership capabilities demonstrated through guiding teams and enforcing best practices in coding and deployment.
Comfortable operating in agile environments and working with cloud platforms like AWS, Azure, or Google Cloud, with a proactive approach to learning and problem-solving.