Data Engineering & Architecture : - Serve as a hands-on technical leader in the design of scalable data pipelines, data stores, and information flows across the enterprise. - Design and optimize cloud-based big data platforms, including ingestion, transformation, storage, and consumption layers. - Lead the engineering of ETL/ELT frameworks, streaming pipelines, and batch processing solutions. - Conduct enterprise-wide assessments of data stores and data flows to identify bottlenecks, friction points, and modernization opportunities. - Own data modeling standards to ensure alignment with business objectives, performance, and accessibility. AI Enablement & Advanced Analytics : - Enable and support AI/ML and GenAI initiatives by building reliable, high-quality, and well-governed data pipelines. - Collaborate with Data Science teams to operationalize models, including feature engineering pipelines, inference data flows, and model monitoring data. - Support AI-driven use cases such as predictive analytics, recommendations, NLP-based insights, and intelligent automation. - Stay current with market trends, embed creative practices into strategy, and drive the organization forward with an AI-first approach ensuring AI initiatives move beyond proof-of-concept to enterprise-scale solutions. - Approach data engineering with an AI mindset and vice versa, reflecting the evolving and inseparable nature of the two disciplines. Delivery, Reliability & Governance : - Ensure teams deliver high-quality solutions with clear requirements, strong engineering discipline, and predictable delivery. - Implement best practices across CI/CD, DevOps, data quality checks, monitoring, and observability. - Embed data governance, security, privacy, and compliance controls across all data platforms. - Ensure platforms meet enterprise standards for availability, scalability, and resiliency. - Lead an enabling team responsible for building foundational platforms, tools, and guardrails,



supporting multiple arms of AI engineering and enabling the broader organization. - Lead multiple scrum pods or functional teams, with accountability for recruitment, upskilling, and technical leadership across the enablement structure. Enterprise Competencies : Learning Agility : - Stays current with rapidly evolving AI, data engineering, and cloud technologies; continuously embeds new knowledge into platform strategy and team practices. - Understands and bridges both data and AI engineering disciplines, adapting quickly as these fields converge. Customer Centricity : - Ensures data platforms and pipelines are designed around the needs of internal teams, end users, and the business delivering reliable, governed, and accessible data products. - Communicates strategy and technical direction with empathy and clarity across all levels, from engineers to executives. Tenacity / Persistence : - Balances empathy with a strong delivery focus drives teams to meet high standards with predictable outcomes even in complex, large-scale environments. - Removes impediments, resolves conflicts constructively, and maintains momentum across multiple teams and workstreams without losing sight of the long-term platform vision. Required Qualifications : - 12 years of experience in data engineering, database engineering, or platform engineering, including 5 years in senior technical leadership roles. - Proven experience leading teams building large-scale data platforms in cloud environments. Deep hands-on expertise with : - 1. Big data ecosystems (Hadoop, Spark, Hive, HDFS, etc.) - 2. ETL/ELT tools and frameworks (Informatica,



DataStage, custom frameworks, etc.) - 3. Relational & non-relational databases (Teradata, Oracle, SQL Server, DB2, Redshift, NoSQL). - 4. Programming & data technologies : SQL, Python, Spark, Scala, Java, shell scripting. - Strong experience with AWS data services (S3, Glue, Athena, RDS, Redshift, etc.). - Solid understanding of distributed systems, data architecture, and performance optimization. - Demonstrated ability to partner with senior stakeholders and influence across technology and business teams. - Hands-on experience with AI technologies; ability to understand and implement new advancements and articulate technical details to both engineering teams and executives. - Financial discipline ability to manage budget and financial responsibilities at a team and platform level. Desired Qualifications : - Experience in financial services, with understanding of consumer and commercial banking data. - Experience supporting or enabling AI/ML and GenAI solutions, including feature pipelines and analytics platforms. - Familiarity with data visualization and BI tools (Tableau, Cognos, SAS). - Knowledge of responsible AI, data governance, and regulatory considerations in highly regulated environments. - Experience modernizing legacy data platforms into cloud-native architectures. - Executive speaking skills ability to articulate strategy, challenge the status quo, and present to senior leadership and key stakeholders with confidence. - Experience working across or within highly collaborative, non-hierarchical organizational cultures with an emphasis on peer relationships and open communication. Education & Certifications : - Required : Bachelor's degree in Computer Science, Engineering, Statistics, or related field. - Preferred : Master's degree in Computer Science, Data Engineering, AI/ML, or related discipline. - Preferred : AWS, Big Data, or Agile certifications.

📌 Principal Engineer- Data And AI (Karnataka)
🏢 Infosys
📍 Karnataka