Data Platform Engineer (SME)

Data Platform Engineer (SME) 

Our enterprise clients are moving from fragmented data foundations to AI-first data platforms capable of supporting large-scale, business-critical AI systems.
AI performance is directly constrained by data quality, availability, governance, and latency.

This role exists to build and operate the data backbone that enables reliable, scalable, and compliant AI at enterprise scale.

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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.

You will position yourself as a Subject Matter Expert capable of pushing AI forward by multiple orders of magnitude, contributing to the construction of one of the most performant AI engineering teams across Europe.