Data Architect - Pyspark

Job Summary Seeking a Principal Data Engineer with strong hands-on expertise in PySpark, Kafka, and Snowflake to design, build, and lead large-scale enterprise data platforms. The role involves driving data engineering architecture, delivering high-volume data solutions, and acting as a senior technical lead across hybrid (on-premise and cloud, preferably AWS) environments. Key Responsibilities • Design and develop scalable batch and streaming data pipelines using PySpark, Kafka, and Snowflake. • Define and implement data & analytics architecture across on-premise and cloud platforms. • Build and manage data lakes, data warehouses, and data marts. • Lead data ingestion, integration, transformation, and orchestration processes. • Implement data governance, lineage, metadata, MDM, and reference data frameworks. • Design and validate data models using Erwin / ER Studio for SQL and NoSQL systems. • Act as primary technical lead for critical programs and integrations. • Collaborate with Agile teams and senior stakeholders to align data strategy with business goals. Required Skills • 14+ years in IT with 12+ years of hands-on data engineering experience. • Expert-level PySpark, Kafka (streaming), and Snowflake. • Strong experience with Python, SQL, NoSQL, Talend, and BODS. • Proven delivery of large-scale, high-volume data platforms. • Experience with hybrid cloud architectures (AWS preferred). • 7+ years working in Agile/Scrum environments. Nice to Have • Experience with QlikView, Qlik Sense, Tableau. • Enterprise data governance and analytics platform exposure.