Job Title: Senior Data Lead Engineer
Location: Malaga, Spain - Hybrid: 1 Day to Office Every Week
Duration: Permanent
Employment Type: Full-Time
Roles & Responsibilities:
We are seeking a Senior Data Lead Engineer to drive the cloud data, AI and BI transformation of our Integral Transaction Banking (GTB) platforms. You will own the data lakehouse, data mesh, and AI enablement roadmap, delivering secure, scalable and business-ready data products across our hybrid (cloud + on-prem) environment.
What you will do
Lead the Data, AI & BI platform strategy for GTB across AWS and SCIB hybrid architecture
Design and evolve the cloud data lakehouse (S3, Iceberg/Delta, EMR, Databricks)
Build domain-oriented data products using data mesh principles (data-as-a-product, SLAs, ownership, contracts)
Design and operate data ingestion, CDC and event-driven pipelines from GTB operational systems
Integrate on-premise data lake with AWS cloud, ensuring catalog, lineage, security and governance
Implement data quality, data rules, normalization and data guardrails
Deliver analytics-ready datasets and semantic layers for BI, KPIs and dashboards
Enable AI/ML and GenAI use cases : feature engineering, training, MLflow, RAG, fine-tuning, monitoring
Provide technical leadership and mentoring across Data, ML and BI engineering teams
Partner with business, product, risk and technology stakeholders to deliver high-impact data solutions
Required Experience
5+ years in Data Engineering, Cloud Data Platforms, AI/ML or Advanced Analytics
Proven experience designing AWS data lakehouse architectures
Hands-on with Databricks or EMR (Spark, Delta, MLflow, feature store)
Strong background in ETL, CDC pipelines and event-driven ingestion
Experience integrating on-prem + cloud data platforms
Experience delivering production AI/ML solutions
Strong involvement in data governance, data quality and data guardrails
Experience working with BI teams, KPIs and semantic data models
Technical Skills
AWS : S3, Glue, Lake Formation, EMR
Databricks : Spark (PySpark/Scala), Delta, MLflow, feature store
Lakehouse & Data Formats : Parquet, Iceberg / Delta, raw → curated → semantic layers
Data Engineering : SQL, Python, ETL, CDC, event-driven pipelines
Data Governance : lineage, catalog, data quality, observability, data rules
BI Enablement : star schemas, semantic layers, KPI modelling
AI/ML & GenAI : feature engineering, training, RAG, fine-tuning, LLM guardrails
Hybrid Architecture : on-prem + AWS aligned with CDAIO standards
Nice to Have
AWS or Databricks certifications
Experience with CI/CD, orchestration, IaC for data platforms
BI tools: QuickSight, Power BI, Qlik
Agile tools: JIRA, Confluence