Principal Data Engineer

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• Design, develop, optimize, and maintain data architecture and pipelines that adhere to ETL principles and business goals
• Define data requirements, gather and mine large-scale structured and unstructured data, and validate data using various data tools in the Big Data environment
• Design and lead the development of AI agents, skills, and MCPs (Model Context Protocols), including defining and enforcing guardrails to ensure responsible, secure, and scalable AI-driven solutions
   
• Support Data Scientists in data sourcing and preparation to visualize data and synthesize insights of commercial value
• Lead the evaluation, implementation, and deployment of emerging tools and processes for analytic data engineering to improve productivity
• Develop and deliver communication and education plans on analytic data engineering capabilities, standards, and processes
• Partner with business analysts and solutions architects to develop technical architectures for strategic enterprise projects and initiatives
• Mentor and coach other data and analytic professionals on data standards and practices
• Design and evolve Data Platform architecture and its core components, ensuring scalability, resilience, and alignment with enterprise data strategy
• Provide cross-team (cross‑pod) technical guidance, particularly on end-to-end data flow design, promoting adoption of approved patterns, standards, and best practices
• Act as a proactive technical SME, unblocking delivery teams through structured support mechanisms and hands-on problem resolution
• Define and align the team’s strategic roadmap with organizational objectives, ensuring key initiatives such as framework scaling, technology assessments, and “Data as a Product” are prioritized for maximum business impact
• Design robust data and solution architectures leveraging technologies such as Delta Live Tables (DLT), API-based ingestion, Databricks OLTP patterns, Kafka, and other modern platform components
• Collaborate closely with leadership to establish scalable engineering standards, including CI/CD standardization, repository structure, reusable framework components, and governance mechanisms that reduce delivery time and operational incidents across teams
   

  Skills and qualifications:

  
  
• Expert in designing and maintaining scalable data architectures, capable of handling complex data systems
• Proficient in SQL and other data manipulation languages, with a strong ability to optimize and troubleshoot queries
• Skilled in DevSecOps practices and continuous integration to enhance workflow efficiencies
• Experienced in software development methodologies, contributing to both project and operational goals
• Knowledgeable in data structures and algorithms, enhancing data processing and storage solutions
• Familiar with distributed data technologies like Spark and Hadoop, capable of managing large-scale data operations
• Efficient in system integration, ensuring seamless data flow and connectivity across platforms
• Understanding Agentic AI skillsets and applying capabilities such as planning, decision-making, tool integration, and autonomous task execution to build robust and high-performance AI solutions.
• Proficient in programming languages such as Python, Scala, Java, and C++, applying them effectively in data engineering tasks
• Experienced in SQL database management, performance tuning, and complex query handling
• Skilled in cloud platform deployment and tools like Kubernetes, optimizing cloud-based data solutions
• Capable of performing advanced troubleshooting and guiding others through complex problem-solving processes