Sr. Data Engineer - INTL India

Define and continuously evolve the data architecture across the Digital Products and Platforms

Translate business and technical goals into scalable and resilient platform designs.

Own and maintain architectural roadmaps, standards, and decision frameworks.

Act as the bridge between architects, SME/Analysts, data engineers, and analytics teams to ensure alignment and compliance with platform standards.

Data Engineering & Platform Delivery

Design and implement modern ELT/ETL pipelines using tools like Spark, Python, SQL, Scala, and cloud-native components (e.g., Databricks).

Design AI ready Data Models (e.g., RAG, agent orchestration, multimodal pipelines) with working reference implementations.

Build reusable AI components, templates, and accelerators to enable consistent adoption across teams.

Implement and optimize scalable, secure, and resilient AI pipelines, aligned with enterprise data and governance standards.

Lead PoC-to-production transitions, ensuring operational readiness, observability, and cost controls

Demonstrated success designing and deploying RAG architectures, including vector stores, embedding strategies, chunking logic, semantic retrieval, and hybrid search

Deep Hands-on experience with at least one of the following Hyperscaler AI / Data Platforms: Azure, AWS

Design and implement Data Modelling using Relational DB/NoSQL DB’s.

Proven Hand-on experience in Databricks & Unity Catalog.

Manage data ingestion from heterogeneous sources including ERP, CRM, IoT, and third-party APIs.

Guide hands-on development of robust, reusable, and automated data flows.

Governance, Metadata, and Quality

Implement and enforce data governance frameworks including data lineage, metadata management, and access controls.

Develop data models (ERDs, dimensional and 3NF) and define canonical data representations.

Collaboration & Leadership

Review solution designs and provide architectural guidance to engineering teams.

Mentor technical staff while fostering best practices and continuous improvement.

Collaborate with DevOps to embed CI/CD, version control, and environment automation across the data lifecycle.

Continuously assess and improve platform reliability, scalability, performance, and cost-efficienc

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5-8+ years of hands-on experience in data architecture and engineering delivery.

Proven success in building modern data platforms on cloud (Azure, AWS, GCP).

Deep knowledge of data lakehouse architectures (e.g., Databricks, Fabric).

Proficiency with Python, SQL, Spark, and orchestration frameworks.

Experience with ETL/ELT tools (e.g., Informatica, Talend, Fivetran) and containerization (Docker, Kubernetes).

Strong background in Data Modeling (ERD, star/snowflake, canonical models).

Familiarity with REST APIs, GraphQL, and event-driven design.

Demonstrated experience integrating AI/ML and GenAI components into data platforms.

Exposure to DataOps and DevOps practices for CI/CD and platform automation. * Working knowledge of code management and CI/CD systems (Azure DevOps or GitHub).

• Familiarity with NoSQL databases (e.g., MongoDB).

• Exposure to IoT Data Standards like Project Haystack, Brick Schema, Real Estate Core.