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, robust 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:
- Big data ecosystems (Hadoop, Spark, Hive, HDFS, etc.)




- ETL/ELT tools and frameworks (Informatica, DataStage, custom frameworks, etc.)
- Relational & non-relational databases (Teradata, Oracle, SQL Server, DB2, Redshift, NoSQL).
- 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.

📌 Infosys - Principal Engineer - Data Engineering & AI Platforms (India)
🏢 EdgeVerve Systems
📍 India