data platform engineer (sme).

Mission 

You will operate as a Subject Matter Expert in complex enterprise environments, designing and delivering AI-ready data platforms where reliability, scalability, lineage, and governance are non-negotiable.

Acting in consultative client engagements, outsourced delivery, or product-based models, you will own the end-to-end lifecycle of data pipelines, from ingestion to serving, while acting as a technical authority on data engineering for AI systems.

Key Responsibilities

Data Engineering (Core)

• Design, build, and maintain scalable batch and streaming data pipelines

• Implement data ingestion from heterogeneous enterprise sources (databases, APIs, events, files)

• Structure data for downstream AI and ML consumption

• Ensure data quality, consistency, and availability across environments
   

AI-Ready Data Platforms

• Build and operate feature stores and analytical data layers for ML

• Design data models optimized for training and inference workloads

• Enable efficient data access patterns for real-time and near-real-time AI use cases

• Support experimentation while enforcing production-grade standards
   

Enterprise-Grade Data Delivery

• Translate business and AI requirements into robust data architectures

• Collaborate closely with AI/ML Engineers, MLOps, Infra, Security, and Product teams

• Implement monitoring, validation, and alerting on data pipelines

• Design lifecycle strategies for data versioning, backfills, and schema evolution

Technical Scope

Data Stack

• Batch and streaming processing

• Data modeling for analytics and AI workloads

• Feature engineering and data serving patterns

Tooling & Engineering

• Python, SQL

• Data processing frameworks (Spark or equivalent)

• Orchestration tools (Airflow, Dagster or equivalent)

• Interaction with data lakes, warehouses, and real-time stores

Production Awareness

• Data reliability, observability, and SLA-driven pipelines

• Cost and performance optimization

• Strong integration with MLOps and inference layers

Profile

Experience

• Strong background in data engineering for large-scale systems

• Proven experience delivering production-grade data pipelines

• Familiarity with enterprise data landscapes and constraints
   

Mindset

• Engineering-first approach to data

• Strong ownership and accountability for data reliability

• Comfortable operating in complex, multi-stakeholder environments

• High standards for robustness, scalability, and maintainability

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This is not a generic ETL role and not a reporting-focused data position.

This role is designed to solve enterprise-grade problems using cutting-edge AI technologies, operating as a technical force multiplier across projects and client environments.