Data Architect + AI

Turtle Trax S.A.; Región Centro; Presencial; MXN 900.000 - 1.500.000; Jornada completa

Descripción de la vacante

Turtle Trax S.A. is seeking a senior Databricks Data Architect to design end-to-end lakehouse pipelines on major cloud platforms, optimizing performance and governance across Bronze, Silver, and Gold layers.

You will drive scalable data solutions and ensure robust data lineage. The ideal candidate has deep Databricks and Spark internals knowledge, Delta Lake ACID guarantees, Unity Catalog governance, and strong SQL with Python or Scala.

Formación

• End-to-end data architectures (Bronze, Silver, Gold)
• Troubleshoot and tune large Spark jobs with caching/partitioning
• ACID, time travel, and Z-ordering in Delta Lake
• RBAC, row/column-level security, data lineage with Unity Catalog
• Cloud platforms: AWS/Azure/GCP and IAM in cloud storage
• Advanced SQL and Python/Scala fluency
• Optimize DBUs, right-size clusters, avoid cloud bill shock

Conocimientos

Lakehouse Architecture

Databricks & Spark Internals

Delta Lake

Unity Catalog & Data Governance

Cloud Infrastructure

SQL

Python or Scala

Cost Optimization

Descripción del empleo

Overall Stack: Databricks Lakehouse Platform, Apache Spark, Delta Lake, Unity Catalog, and modern cloud data architecture

Must Have

• Lakehouse & Medallion Architecture: Expertise in designing end-to-end data architectures (Bronze, Silver, Gold layers) for reliable, production-ready pipelines
• Databricks & Spark Internals: Deep understanding of distributed computing. They must know how to troubleshoot and tune large-scale Spark jobs using caching, partitioning, and broadcast joins
• Delta Lake: Must understand ACID transactions, schema enforcement, time travel, and optimization operations like Z-ordering
• Unity Catalog & Data Governance: Proven ability to design unified governance models for data and AI assets, including role-based access control (RBAC), row/column-level security, and data lineage
• Cloud Infrastructure (AWS, Azure, or GCP): Strong grasp of the native cloud ecosystem they work in (e.g., ADLS/Entra for Azure, S3/IAM for AWS), including Virtual Network (VNet) setups and IAM roles
• Coding Proficiency: Advanced SQL skills and fluency in Python or Scala
• Cost Optimization & Performance Tuning: Ability to monitor DBUs (Databricks Units), right-size serverless and multi-node clusters, and implement best practices for avoiding cloud bill shock

Nice to Have

• Databricks Certifications: Candidates holding valid Databricks Certified Data Architect or Databricks Certified Data Engineer Professional badges generally have a proven, up-to-date baseline of the platform's features
• Generative AI & MLflow Integration: Experience building, deploying, and monitoring GenAI applications and ML models using Databricks Model Serving, Vector Search, and the Mosaic AI suite
• CI/CD & DevOps Practices: Experience automating Databricks workflows using Git (Databricks Repos) and orchestration tools like dbt, Azure Data Factory, or Apache Airflow
• Streaming Data: Familiarity with Databricks Structured Streaming and Auto Loader for real-time data ingestion and processing
• Data Warehousing & BI: Understanding of Databricks SQL, Serverless Warehouses, and integration with downstream BI tools like Power BI

Remote

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