
Strong Databricks Architect Profile with end-to-end Lakehouse ownership
2
Mandatory (Experience 1) – Must have 10+ years of software engineering experience with atleast 5+ years in Data Engineering with hands on exposure to Databricks and strong ownership of end-to-end data pipeline development.
3
Mandatory (Experience 2) – Must have atleast 5+ years of expertise across the Databricks ecosystem — Delta Lake, Delta Live Tables, Autoloader, Structured Streaming, Workflows, Unity Catalog
4
Mandatory (Tech skill 1) – Must have worked at architecture level, owning end-to-end design through deployment
5
Mandatory (Tech skill 2) – Must have strong experience with Python and SQL for data processing and Apache Spark for performance tuning & scalability
6
Mandatory (Tech skill 3) – Must have experience in large-scale data warehousing & advanced data modeling (3NF and dimensional) across batch and real-time systems
7
Mandatory (AI Exposure) – Must have at least a basic working understanding of how AI services or tools work
8
Mandatory (Communication) – Must have strong stakeholder management & requirement-gathering experience with US or UK clients
9
Mandatory (Company) – Must come from a B2B IT services or IT consulting background
10
Mandatory (Note) – CTC is inclusive of 5% variable
11
Preferred (Tech skill 1) – Azure Databricks or Azure data services experience (project runs on Azure DevOps)
12
Preferred (Tech skill 2) – MLflow or MLOps practices and AI use cases (RAG, AI/BI)
13
Preferred (Tech skill 3) – CI/CD, Databricks Asset Bundles (DABs) or equivalent packaging, Terraform or IaC, reusable deployment templates
14
Preferred (Integrations) – ServiceNow or enterprise system integrations
15
Preferred (Certifications) – Databricks (Data Engineer Associate or Professional, ML or GenAI tracks), Azure or AWS cloud certifications

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