Principal Data Lead (Data Engineering with Full-Stack Dev) - Investment Systems
Gurugram · 12+ years experience · Posted Oct 13, 2026
At a glance
- Experience
- 12+ years experience
- Location
- Gurugram
About the role
- The Principal Data Lead will set the technical direction for the firm's Reference Data Platform, serving as the senior-most engineering voice for master data management.
- This hands-on leadership role involves architecting an end-to-end, event-driven data platform that handles sourcing, ingestion, entity mastering, and API-first distribution.
- The role requires driving platform strategy while partnering with product, data, and infrastructure leaders to deliver scalable, vendor-agnostic capabilities.
Responsibilities
- Own the architecture of a layered reference-data platform, defining contracts between sourcing, ingestion, mastering, golden-copy, and distribution layers.
- Set technical direction for entity mastering, including standardization, blocking, matching, survivorship, and crosswalk/lineage.
- Architect a bitemporal, point-in-time golden copy source of truth with per-attribute provenance.
- Define the event-driven backbone and cadence model to support both batch and real-time streaming.
- Design an API-first, self-documenting distribution surface governed by data contracts.
- Drive storage and compute strategy using an open lakehouse and elastic compute.
- Establish engineering standards, reference patterns, and a reusable component library.
- Lead cross-team technical design reviews and resolve architectural trade-offs.
- Champion data quality, governance, security, entitlements, audit, and observability.
- Mentor senior and staff engineers while aligning engineering execution with the firm's data strategy.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- 12+ years of professional software engineering experience, including significant time as a technical lead or architect of data-intensive platforms.
- Expert-level proficiency in Python for building production services, data pipelines, and platform frameworks.
- Proven experience architecting master data management (MDM) or reference/entity-data systems.
- Deep data engineering expertise in data modeling, ETL/ELT design, distributed processing (Spark), and data distribution at scale.
- Hands-on experience designing API-first services and event-driven/streaming architectures.
- Strong command of modern data storage including relational, non-relational, and lakehouse formats and SQL.
- Experience with infrastructure-as-code (Terraform) and cloud-native deployment.
- Deep domain experience with reference and master data in the financial industry.
- Practical experience applying Generative AI/LLMs to data platforms.
Preferred qualifications
- Familiarity with bitemporal/point-in-time data modeling and governed, auditable data systems.
- Experience with workflow orchestration (e.g., Dagster, Airflow) and lakehouse catalogs.
- Exposure to commercial or vendor MDM/ontology platforms (e.g., Palantir Foundry).
- Experience building agentic workflows or retrieval-augmented systems on enterprise data.
- Familiarity with LLM evaluation, guardrails, and grounding techniques.
- Experience with legal-entity identifiers and standards (e.g., LEI, GLEIF) and regulatory reference-data requirements.
Skills
- Python
- Spark
- Kafka
- Apache Iceberg
- Snowflake
- Terraform
- Kubernetes
- EKS
- SQL
- Generative AI
- LLMs
- CDC
Show all 13 skills
- REST
