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Location: Barcelona, Spain.

Contract role.

Hybrid 3 days a week from office.

Job summary

We are seeking a hands‑on AI Platform Engineer to design, build, and operate Databricks‑based data and AI platforms on AWS.

You will enhance AI capabilities by leveraging Databricks (Workspace, Unity Catalog, Lakehouse, MLflow), modern cloud services, and Dev

Ops/MLOps practices to deliver reliable, secure, and scalable the role

As an AI Platform Engineer, you will invent and industrialize how the Project team uses Databricks, machine learning, and data.

You will build, deploy, and evolve next‑generation platform capabilities at scale, partnering with data scientists, ML engineers, and platform teams to deliver a best‑in‑class developer experience on the Lakehouse.

What candidate is needed

Proficient in cloud operations on AWS, with strong understanding of scaling infrastructure and optimizing cost/performance.

Proven hands‑on experience with Databricks on AWS: workspace administration, cluster and pool management, job orchestration (Jobs/Workflows), repos, secrets, and experience with Databricks Unity Catalog: metastore setup, catalogs/schemas, data lineage, access control (ACLs, grants), attribute‑based access control, and data governance.

Expertise in Infrastructure as Code for Databricks and AWS using Terraform (databricks and aws providers) and/or AWS Cloud

Formation; experience with Databricks asset bundles or CLI is a plus.

Experience implementing CI/CD and Git

Ops for notebooks, jobs, and ML assets using Git

Hub and Git

Hub Actions (or Git

Lab/Jenkins), including automated testing and promotion across workspaces.

Ability to structure reusable libraries, package and version code, and enforce quality via unit/integration tests and linting.

Proficiency with SQL for Lakehouse development.

Experiment tracking, model registry, model versioning, approval gates, and deployment to batch/real‑time endpoints (Model Serving).AWS IAM/STS, Private

Link/VPC, KMS encryption, Secrets, SSO/SCIM provisioning,



and monitoring/observability (Cloud

Watch/Datadog/Grafana).

Experience with Dev

Ops practices to enable automation strategies and reduce manual or awareness of MLOps practices; building pipelines to accelerate and automate machine learning will be viewed favorably.

Excellent communication, cross‑functional collaboration, and stakeholder management skills.

Detail‑oriented, proactive, able to work independently and within a distributed team.

What candidate will do

Design and implement scalable Databricks platform solutions to support analytics, ML, and GenAI workflows across environments (dev/test/prod).

Administer and optimize Databricks workspaces: cluster policies, pools, job clusters vs. all‑purpose clusters, autoscaling, spot/fleet usage, and GPU/accelerated compute where applicable.

Implement Unity Catalog governance: define metastores, catalogs, schemas, data sharing, row/column masking, lineage, and access controls; integrate with enterprise identity and audit.

Build IaC for reproducible platform provisioning and configuration using Terraform; manage config‑as‑code for cluster policies, jobs, repos, service principals, and secret scopes.

Implement CI/CD for notebooks, libraries, DLT pipelines, and ML assets; automate testing,quality gates, and promotion across workspaces using Git

Hub Actions and Databricks APIs.

Standardize experiment structure, implement model registry workflows, and deploy/operate model serving endpoints with monitoring and rollback.

Develop and optimize Delta Lake pipelines (batch and streaming) using Auto Loader, Structured Streaming, and DLT; enforce data quality and SLAs with expectations and alerts.

Optimize cost and performance: rightsize clusters and pools, enforce cluster policies and quotas, manage DBU consumption,



leverage spot/fleet, and implement chargeback/showback reporting.

Integrate observability: metrics/logs/traces for jobs, clusters, and model serving; configure alerting, on‑call runbooks, and incident response to reduce MTTR.

Ensure platform security and compliance: VPC design, Private

Link, encryption at rest/in transit, secrets management, vulnerability remediation, and audit readiness; align with internal security standards and, where applicable, GxP controls.

Collaborate with cross‑functional teams to integrate the Databricks platform with data sources, event streams, downstream applications, and AI services on AWS.

Conduct technical research, evaluate new Databricks features (e.G., Lakehouse Federation, Vector Search, Mosaic AI), and propose platform improvements aligned to roadmap.

Regularly communicate progress, risks, and recommendations to client managers and development teams.

Required qualifications

Hands‑on Databricks administration on AWS, including Unity Catalog governance and enterprise AWS foundation: networking (VPC, subnets, SGs), IAM roles and policies, KMS, S3, Cloud

Watch; EKS familiarity is a plus but not required for this Databricks‑focused role.

Proficiency with Terraform (including databricks provider), Git

Hub, and Git

Hub Actions.

Strong Python and SQL; experience packaging libraries and working with notebooks and repos.

Experience with MLflow for tracking and model registry; experience with model serving endpoints preferred.

Familiarity with Delta Lake, Auto Loader, Structured Streaming, and DLT.

Experience implementing Dev

Ops automation and runbooks; comfort with RESTAPIs and Databricks CLI.

Git and Git

Hub proficiency; code review and branching level

Mid‑Senior level

Employment type

Contract

Job function

Analyst

IndustriesIT Services and IT Consulting and Data Infrastructure and Analytics

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