Databricks Solution Architect

Celebal Technologies; Dallas (TX); On-site; USD 140,000 - 190,000; Full time

Job summary

Celebal Technologies is seeking a hands-on Senior Data Engineer to build and scale an enterprise data platform on Databricks + AWS. The goal is to turn messy operational data into trusted, reusable, AI-ready data products powering Tableau, ML, and self-service analytics across multiple business domains.

The role requires production Databricks experience, advanced SQL+Python, and strong Spark/PySpark skills.

Qualifications

• 8+ years of hands-on data engineering at enterprise scale.
• Production Databricks experience required.
• Proficient in SQL and Python with Spark/PySpark.
• Hands-on with Delta Lake and Bronze/Silver/Gold architecture.
• Knowledge of Unity Catalog, data governance, lineage, access controls.
• Experience with Git, CI/CD, and DataOps practices.
• Ability to own requirements from analysis to production.

Responsibilities

• Build and scale an enterprise data platform on Databricks + AWS.
• Turn messy operational data into trusted, reusable, AI-ready data products.
• Develop a governed single source of truth for analytics across business domains.
• Collaborate with architects and stakeholders to design data products for AI and analytics.

Skills

Databricks

SQL

Python

Spark

Delta Lake

Medallion architecture

Unity Catalog

Data governance

Git CI/CD

DataOps

End-to-end ownership

Job description

Key factors to evaluate the candidate on

Client wants a hands-on Senior Data Engineer to build and scale their enterprise data platform on Databricks + AWS. The core goal isn't just moving data around - it's turning messy operational data into trusted, reusable, "AI-ready" data products that power Tableau, Databricks Genie/Genie Code, ML, and self-service analytics across Manufacturing, Supply Chain, Sales, Finance, and HR. They're trying to shift from one-off pipelines and reports toward a governed, single-source-of-truth platform (Databricks).

The non-negotiables (screen hard here):

• 8+ years hands-on data engineering at enterprise scale


• Production Databricks experience — not just training/POCs


• Advanced SQL + Python, and strong Spark/PySpark


• Delta Lake and Medallion architecture (Bronze/Silver/Gold) hands-on


• Unity Catalog, data governance, lineage, access controls


• Git, CI/CD, DataOps practices


• Can independently own a requirement from source analysis → production

The differentiators (what separates a good fit from a great one):

• Experience building data foundations specifically for Genie / conversational / self-service AI analytics, and designing semantic/contextual layers for AI — this is the standout theme and relatively rare, so weight it heavily


• Exposure to ML / feature engineering / MLOps workloads


• Data quality, reconciliation, monitoring, and production support discipline

Nice-to-haves (tiebreakers, not deal-breakers):

• Manufacturing/MES data (WIP, yield, cycle time, test/quality data)


• ERP/CRM/PLM/MES or Oracle/SQL Server integration experience


• Tableau


• Legacy ETL/warehouse → cloud Lakehouse modernization

One thing worth probing in interviews: because this is a platform-building role (reusable frameworks, not one-off pipelines), test for architectural/design thinking and communication with business stakeholders.