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 innovative 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 current 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 (Vasanth Nagar)
🏢 Infosys
📍 Vasanth Nagar