Senior Data Lead Engineer

As a Senior Data Lead Engineer, your mission will be to lead the design, evolution and operation of cloud-based Data, AI & BI platforms, enabling scalable, secure and high-quality data products that drive business value.

You will play a key role in defining the data roadmap, mentoring engineering teams and delivering advanced analytics and AI use cases in a complex and large-scale environment.

We need someone like you to contribute across the following responsibilities:

• Lead the Data, AI & BI roadmap, ensuring scalability, resilience, security and cost efficiency.
• Design, evolve and operate cloud data lakehouse architectures.
• Define and build domain-oriented data products aligned with data mesh principles (data-as-a-product, SLAs, ownership).
• Build and maintain data ingestion, ETL and transformation pipelines, including CDC-based and event-driven architectures.
• Integrate cloud platforms with on-premise data platforms in hybrid environments.
• Implement and enforce data governance, data quality rules and data guardrails.
• Deliver high-quality, well-modelled datasets and semantic layers for BI, reporting and analytics.
• Enable AI/ML and LLM use cases (feature engineering, training, RAG, fine-tuning, monitoring).
• Promote engineering best practices and act as a technical leader and mentor for data, ML and BI engineers.
• Collaborate with product, technology and business teams to prioritise and deliver high-impact initiatives.

• 5+ years of experience in Data Engineering, Data Platforms, AI Engineering or Advanced Analytics.
• Proven experience designing and building cloud data platforms and lakehouse architectures (preferably AWS).
• Hands-on experience with Databricks or EMR for large-scale data processing.
• Strong background in data ingestion, ETL and CDC-based pipelines.
• Experience working with hybrid architectures (on-premise + cloud).
• Experience enabling AI/ML solutions in production.
• Experience collaborating with BI teams and business stakeholders on data modelling and KPI definition.

EDUCATION

• Bachelor's degree or higher in Computer Science, Engineering, Mathematics or a related technical discipline.
• Additional training in Data Engineering, AI/ML or Analytics is a plus.

SKILLS & KNOWLEDGE

• AWS: S3, Lake Formation, Glue, EMR.
• Databricks: Spark (PySpark/Scala), Delta tables, MLflow, feature store, performance optimisation.
• Strong SQL and Python skills for data processing and automation.
• Data formats and lakehouse concepts: Parquet, Iceberg / Delta, curated layers.
• Experience with data quality, lineage, observability and monitoring.
• Knowledge of CDC patterns and event-driven ingestion.
• Understanding of data mesh principles and federated governance.
• Experience supporting ML workflows (feature engineering, training, deployment, monitoring).
• Knowledge of LLMs (prompt engineering, fine-tuning, RAG, evaluation and guardrails).
• Strong understanding of BI concepts, semantic modelling and analytics consumption.
• Practical experience with data governance, data rules and data guardrails.

SOFT SKILLS

• Strong communication skills, able to explain complex data and AI topics to technical and non-technical audiences.
• Ability to influence and align multiple teams without direct authority.
• Proven leadership and mentoring capabilities.
• Proactive, hands-on and outcome-oriented mindset.
• Collaborative and adaptable in complex environments.

OTHER INFORMATION / NICE TO HAVE

• AWS or Databricks certifications.
• Experience with orchestration tools and CI/CD for data and ML pipelines.
• Knowledge of Infrastructure as Code.
• Experience with BI tools such as QuickSight, Power BI or Qlik.
• Experience working with Agile methodologies (JIRA, Confluence).