Lead Data Engineer

POSITION DESCRIPTION

JOB TITLE Lead-Data Engineer

GRADE VP

DEPARTMENT DATA SCIENCE AND DECISION MANAGEMENT

LOCATION HO(Bengaluru)

SUB-DEPARTMENT  DSDM

TYPE OF POSITION Full-time

REPORTS TO

REPORTING INTO Senior Data Engineers, Data Engineers, Senior Data Quality Analyst/ Data Quality Analyst

 ROLE PURPOSE & OBJECTIVE

The Lead-Data Engineer will be responsible for architecting, building, and governing enterprise-scale data pipelines and platforms for Ujjivan Small Finance Bank. The role ensures secure, high-quality, reliable, and timely data availability to support analytics, regulatory reporting, risk management, and AI/ML initiatives.

This role provides technical and people leadership, defines data engineering standards, and acts as a key interface between business, analytics, governance, and technology teams.

 KEY DUTIES & RESPONSIBILITIES OF THE ROLE

1. Business/ Financials

• Design and own end-to-end data pipeline architecture across batch and near real-time processing aligned to enterprise strategy.
• Define and govern bronze, silver, and gold data layer architecture for enterprise consumption.
• Enable analytics, ML, and AI use cases by delivering model-ready and feature-ready datasets that drive business outcomes.
• Optimize data pipeline performance and cost efficiency.
• Establish CI/CD pipelines for data engineering, including version control, testing, and controlled deployments.
• Contribute to planning, budgeting, and prioritization of data engineering initiatives aligned to business goals. 

1. Customer (Both Internal & External)

• Collaborate with business, analytics, and risk teams to translate requirements into scalable data solutions.
• Lead ingestion of data from Core Banking, LOS, LMS, Collections, CRM, Payments, Finance, and external data sources to support internal and external consumers.
• Enable timely, reliable, and high-quality data availability for stakeholders across the organization.
• Partner with Data Quality & Governance teams to operationalize Critical Data Elements (CDEs), lineage, and metadata for stakeholder trust and usability.

1. Internal Process

• Ensure pipeline scalability, fault tolerance, restartability, and SLA adherence.
• Implement workflow orchestration, dependency management, backfills, and automated retries.
• Embed automated data quality checks, reconciliation controls, and anomaly detection.
• Ensure secure data handling, including masking, encryption, and role-based access control.
• Ensure compliance with regulatory, audit, and information security requirements.
• Comply with internal SLAs, policies, and standard operating procedures.
• Drive process management and continuous process excellence across data engineering workflows

1. Innovation & Learning

• Upskill team members to new age technologies with respect to machine learning and credit modelling. Enhance on-job self-learning related to analytics techniques/tools
• Ensure timely completion of training / learning programs assigned time to time

 MINIMUM REQUIREMENTS OF KNOWLEDGE & SKILLS

Educational

Qualifications

• Bachelor’s or Master’s degree in engineering, Computer Science, or related field 

Experience Range (Years and Core Experience Type)

• 12-15 years of experience in data engineering or large-scale data platform development.
• Proven experience in banking or financial services data environments.
• Demonstrated experience leading teams and enterprise data programs.

Certifications

• NA/ Good to have 

Functional Skills

• Advanced SQL and strong programming skills in Python / Scala and pyspark.
• Deep understanding of Cloud architecture and Devops
• Strong experience with ETL/ELT frameworks and distributed data processing.
• Hands-on experience with data orchestration and scheduling frameworks.
• Deep understanding of data warehousing, data lakes, and layered data architectures.
• Expertise in data quality, reconciliation, metadata management, and data lineage.
• Strong knowledge of CI/CD, version control (Git), and automated testing for data pipelines.
• Experience with data security, masking, encryption, and role-based access control.
• Exposure to streaming or near real-time data processing is desirable.
• Understanding of ML/AI data requirements and feature engineering pipelines 

Behavioral Skills

• Leadership skills to manage a team of data quality Analysts and data engineers
• Excellent listening, interpersonal, communication and problem-solving skills
• Demonstrated ability to work effectively in teams, in both a lead and support role
• Effective time management skills, including demonstrated ability to manage and prioritize multiple tasks and projects
• Ability to build strong relationships both internally and externally
• Exceptionally strong organizational, problem-solving and communication skills
• Structured thinker, effective communicator with excellent written communication skills 

Competencies

• A passion for constantly learning and applying new technologies and programming languages in a constantly evolving environment
• Demonstrated ability to learn new techniques and troubleshoot code without support
• Prior people management experience, with tried-and-true approaches for mentoring junior staff
• Understanding of the financial sector, to recognize and drive forward opportunities where data engineering can transform the bank
• Skilled at influencing and communicating to various stakeholders up to executive level