Data Platform Engineering Lead

Level: M / SA

Role Overview

A hands-on technical lead responsible for building AI-ready enterprise data platforms, including scalable pipelines, lakehouse architectures, and governed data foundations that enable model training, evaluation, and inference. The role extends to backend services and integration layers that expose curated datasets, feature stores, and data products for AI/ML and LLM-driven applications.

Key Responsibilities

Build & Deploy Data Platform Solutions
Design and develop data pipelines for batch and streaming workloads using Python and Spark/PySpark
Build and manage AI-ready datasets, feature pipelines, and training data foundations
Develop backend services and APIs (Python – FastAPI or equivalent) to expose data products
Implement microservices and event-driven architectures for data ingestion, processing, and serving
Ensure the platform is scalable, performant, and maintainable
Mentor engineers on data engineering patterns and best practices.
Enable AI/ML & LLM Integration
Develop AI-ready pipelines for model training, evaluation, and inference
Enable feature store capabilities and reproducible datasets
Support integration with LLM/AI services (RAG, embeddings, inference APIs)
Enable data-to-AI pipelines including vectorization and retrieval workflows.
Oversee Implementation on Cloud Infrastructure
Collaborate with infra teams on Azure, AWS, or GCP
Build and integrate data lakes, lake-houses, SQL/NoSQL systems
Enable integration between data platforms and AI/ML systems
Implement containerized and serverless architectures.
Implement Modern Software Engineering Practices
Implement CI/CD pipelines and observability
Define and enforce data quality frameworks
Support metadata, lineage, and governance
Optimize platform performance, reliability, and scalability.

Required Capabilities / Skills / Experience

8+ years in data engineering and backend development
Strong Python, APIs, and microservices experience
Deep experience in Spark/PySpark or equivalent
Expertise in ETL/ELT pipelines and lakehouse architectures
Experience with feature stores and AI-ready datasets
Experience with Azure/AWS/GCP
Familiarity with Docker, Kubernetes, CI/CD
Familiarity with LLM/GenAI integration patterns
Familiarity with metadata and governance tooling
Strong system design and problem-solving skills.

Data Platform Engineering Lead - AITDS