JOB DESCRIPTION
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 RESPONSIBILITIES OF THE ROLE
- 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.
- 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.
- Internal Process
- Ensure pipeline scalability, fault tolerance, restart ability, 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
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
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
