Build and maintain scalable ETL/ELT pipelines, reusable data models, data APIs, and data lake infrastructure. Define product KPIs and measurement frameworks, enable funnel and monetization analytics, and support batch and real-time data processing. Ensure data quality, governance, monitoring, and documentation while collaborating with product, engineering, analytics, and business teams. Mentor junior staff and adopt emerging data engineering and AI/ML technologies.
Key Responsibilities:
Partner with Product Managers to define KPIs, feature success metrics, and measurement frameworks.
Collaborate with Tech Owners to ensure accurate and scalable data instrumentation.
Work closely with Business teams to convert commercial goals into structured, analytics-ready datasets.
Build and maintain ETL/ELT pipelines and reusable data models.
Enable funnel, retention, cohort, and monetization analysis.
Implement and support data lake architecture, covering:
Data at rest (storage, warehousing, governance)
Data in motion (real-time ingestion, streaming, transformation)
Ensure data quality, monitoring, documentation, and governance standards.
Required Skills
Strong SQL and hands-on Python expertise.
Experience with modern data warehouses (Snowflake / BigQuery / Redshift).
ETL orchestration tools (Airflow / DBT or similar).
Understanding of streaming and real-time data concepts.
Experience working in cross-functional product environments.
Critical Requirement – Learning Agility
Readiness and capability to learn and adopt any technology required for the role.
Hands-on across modules required for data lake implementation.
Ability to evaluate, adapt, and implement evolving data technologies as product complexity scales.
15. Experience in building and maintaining data APIs or data-serving layers for downstream applications.
16. Collaborative mindset with the ability to work closely with engineering, product, and analytics teams.
17. Awareness of emerging trends in data engineering, AI/ML, and data infrastructure.
18. Capability to mentor junior team members and contribute to team skill development.
19. Strong documentation practices to ensure knowledge sharing and system maintainability.
20. AI-enabled mindset with the ability to leverage AI/ML tools and insights to drive smarter decision-making, improve data workflows, and enhance product outcomes.
21. AI-enabled mindset with the ability to leverage AI/ML tools and insights.
22. Continuous learning attitude with readiness to adopt new technologies.
23. Awareness of emerging trends in data engineering and data infrastructure.
What We Value
Product-first mindset with strong technical grounding.
Ownership and accountability.
Clear communication with business and technology stakeholders.
Structured thinking and bias for action.
National Payments Corporation Of India (NPCI) Mumbai, Maharashtra, IND Office
The Capital, B Wing, 10th Floor,, Bandra Kurla Comple, Mumbai, Maharashtra , India, 400051
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