The role
La información a continuación detalla los requisitos del puesto, la experiencia esperada del candidato y las cualificaciones correspondientes.
Build the prioritisation engine:
business rules, objective function and expected-value scoring per company/product.
Architect data pipelines on AWS
(Glue, Step Functions, Lambda, S3, Athena, Bedrock) that replace static lists and manual CRM/Excel crosses with automated opportunity identification.
Lead a cross-functional team of Data Engineers and Data Scientists, mentoring them on architecture, data quality and the integration of scoring/ML models into production.
Build full traceability into the system, target perimeter vs. covered perimeter.
Design the criteria-governance and market-intelligence layers as a reusable standard across business units, banks and countries.
Collaborate closely with Product and Business teams
to turn real banking pain points into a robust, sellable product.
What we're looking for
Strong
quantitative and analytical mindset, comfortable translating business criteria into scoring and prioritisation logic.
At least
5 years of experience in data-intensive roles
(Data Engineering, Analytics or Data Science), with at least
2 years leading or coordinating technical teams.
Solid experience designing and operating
AWS cloud architectures for data workloads
(S3, Glue, Lambda, Step Functions, Athena,



RDS).
Expert knowledge of
Python for data engineering and analytical applications;
SQL proficiency for complex querying and data modelling.
Demonstrated experience building scoring, prioritisation or recommendation systems that combine business rules with data-driven models (next-best-action, lead scoring, opportunity prioritisation, or equivalent).
Fluency in Spanish and English.
This gives extra points
Background in
Mathematics, Operations Research, Statistics, or other quantitative disciplines.
Hands‑on experience with constrained/combinatorial optimisation, multi‑armed bandits, or reinforcement learning applied to business decisioning.
Experience with Big Data frameworks ( PySpark, Glue ) and distributed data processing. xhfqzwm
Relevant experience with CI/CD pipelines, infrastructure-as-code ( Terraform/CDK ), and container environments (Docker, ECS).
Experience in
Financial Services, banking CRM/commercial platforms, credit risk, or marketing decisioning .
Previous experience in a startup or fast‑paced environment building multi‑country/multi‑tenant products from scratch, where ownership and autonomy are key.
What we offer
25 days of vacation
(and your
birthday off! )
Hybrid
mode: 2 days in office 3 days in remote
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📌 Data Lead (Madrid)
🏢 mscope
📍 Madrid