About the role

Data Architect + AI
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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Originally posted on Himalayas

Interview prep

Walk in with sharper answers.

Use this as a quick practice sheet before you speak with the employer.

Senior

Technology & IT API integration Data Analysis MySQL Operations Python Senior level

Likely questions

Tell us about work you have done that is close to the Data Architect + AI role.
How would you approach your first 30 days at Valce Talent Solutions?
Which of API integration, Data Analysis and MySQL have you used recently, and what did it help you achieve?
How have you led people, improved a process, or made a hard decision in a previous role?
How do you handle busy days, changing priorities, or pressure at work?

Prepare before the call

A recent example that proves your experience with API integration, Data Analysis and MySQL.
One short story with a problem, your action, and the result.
Two examples that show the strengths listed on your CV.
A clear reason why this role and company interest you.
Your availability, preferred work style, and salary expectations.

Ask them

What would success look like in the first 90 days?
What are the main problems this hire should help solve?
How does the team give feedback and measure good work?
What does a normal working week look like for this role?

Practice line

I am interested in the Data Architect + AI role because I can bring practical experience in API integration, Data Analysis and MySQL, learn the team quickly, and contribute to the outcomes Valce Talent Solutions needs from this hire.

Data Architect + AI

About the role

Walk in with sharper answers.

Likely questions

Prepare before the call

Ask them

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