As Senior Data Lead Engineer in GTB (SCIB), from our Málaga office you will drive the evolution of our data, AI and BI platforms on the cloud.
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You will:
· Lead the Data, AI & BI roadmap for GTB ensuring scalability, resilience, security and cost efficiency.
· Design and evolve our data lakehouse architecture.
· Define and build domain-oriented data products aligned with data mesh principles (data-as-a-product, SLAs, contracts, domain ownership).
· Build and maintain data ingestion, ETL and transformation pipelines, including CDC-based and event-driven ingestion.
· Integrate cloud platforms with the SCIB on-premise data lake, ensuring cataloguing, lineage, governance and security follow CDAIO best practices.
· Implement and enforce data governance, data rules, data cleaning/normalisation and data guardrails.
· Provide high-quality, well-modelled datasets and semantic layers for BI, KPI definition and data visualisationin collaboration with BI and business teams.
· Enable AI/ML and LLM use cases (training, feature engineering, RAG, fine-tuning, guardrails, monitoring).
· Promote engineering best practices and act as a technical leader and mentor for data, ML and BI engineers.
· Work closely with GTB and SCIB stakeholders to prioritise and deliver high-impact data and AI initiatives.
EXPERIENCE
· 5+ years in Data Engineering / Data Platform / AI Engineering / Advanced Analytics, ideally in large or regulated organisations.
· Proven experience designing and building cloud data platforms and data lakehouse architectures (preferably AWS).
· Hands-on experience with Databricks or EMR for large-scale data transformation.
· Strong background in data ingestion and ETL.
· Experience with CDC-based pipelines and event-driven architectures for ingesting operational/transactional data.
· Experience integrating on-premise data lakes with cloud platforms in hybrid architectures.
· Experience enabling AI/ML solutions in production.
· Hands-on involvement in data governance, data quality, data rules and guardrails.
· Experience working with BI and business stakeholders on KPI design and data modelling for reporting/visualisation.
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 (Jobs, Crawlers, Data Wrangler), EMR.
· Data formats and lakehouse: Parquet, Apache Iceberg / Delta-style, curated layers (raw, curated, semantic).
· Databricks: Spark (PySpark/Scala), notebooks, clusters, jobs, Delta tables, performance optimisation, MLflow, feature store.
· Strong SQL and Python for data processing, ETL and automation.
· Experience with data quality, lineage and observability (metrics, logging, alerts, data tests).
· Design of data lakehouse architectures (separation of storage/compute, multi-zone design).
· CDC patterns to ingest and keep synchronised changes from GTB operational systems.
· Understanding of data mesh: domain ownership, data-as-a-product, federated governance.
· Design and operation of hybrid (on-prem + cloud) architectures integrated with SCIB's data lake, aligned with CDAIO.
· General knowledge of machine learning algorithms (regression, classification, clustering, time-series, recommendations, anomaly detection).
· Experience supporting ML workflows (feature engineering, training, validation, deployment, monitoring) on Databricks ML, EMR (Spark ML) or SageMaker.
· Knowledge of LLM training and adaptation (prompt engineering, fine-tuning, RAG, evaluation and feedback loops).
· Familiarity with Quick Suite and Amazon Bedrock to expose LLM capabilities (Q&A, summarisation, agents) with appropriate guardrails and risk controls.
· Strong understanding of BI concepts and how data is consumed in dashboards and reports.
· Experience working with business stakeholders to define KPIs and metrics (volumes, balances, revenues, risk, SLAs, operational KPIs).
· Ability to design semantic/logical data layers optimised for BI tools (star schemas, wide tables, aggregation layers).
· Awareness of data storytelling and visual best practices (drill-down, segmentation, trend/exception views).
· Practical understanding of data governance in large organisations.
· Experience defining and implementing data rules (validations, thresholds, completeness, timeliness, referential integrity).
· Hands-on work in data cleaning, standardising and normalising datasets.
· Knowledge of data guardrails: access control, masking, anonymisation, segregation of environments, safe AI/analytics usage.
SOFT SKILLS
· Strong communication skills, able to explain data, AI and architecture topics to technical and non-technical audiences.
· Ability to influence and align multiple teams without direct authority.
· Proven leadership and mentoring of data, ML and BI engineers.
· Proactive, hands-on, outcome-oriented mindset focused on business value and reliability.
· High adaptability and resilience in a complex, global and regulated environment.
· Collaborative and team-oriented, building trusted relationships (business, product, risk, technology).
NICE TO HAVE
· AWS certifications: Data Analytics, Machine Learning, Solutions Architect.
· Databricks certifications (e.g. Data Engineer, Machine Learning).
· Experience with orchestration tools and CI/CD for data and ML pipelines.
· Knowledge of Infrastructure as Code for data & AI platforms.
· Experience with BI tools (QuickSight, Power BI, Qlik, etc.) from a data provider perspective. xohynlm
· Experience working with Agile methodologies and tooling (JIRA, Confluence).