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.
Qualifications
EXPERIENCE
-
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.
聽
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).