Databricks Solution Architect
Remote · USFull-timePosted 1 day agoStill listed today
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At a glance
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Job overview
Celebal Technologies seeks a hands-on Senior Data Engineer to build and scale an enterprise data platform on Databricks and AWS. The role turns operational data into governed, reusable, AI-ready data products for analytics, machine learning, and self-service use across business functions. The candidate will help move the organization from one-off pipelines and reports toward a single-source-of-truth platform.
Skills & qualifications
Skills
Qualifications
Full job description
Title - Databricks Solution Architect Location -Remote Duration – FTE 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).
Key factors to evaluate the candidate on 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 Dimensional / enterprise data modeling (facts, dimensions, semantic layers) Unity Catalog, data governance, lineage, access controls Git, CI/CD, DataOps practices AWS (or comparable major cloud) 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.
How this job compares
Set against the 2,600 open Data Engineering jobs on Olive Jobs, counted October 28, 2026.
- Pay
- This postingNot listed
- Texas median$104–201K/yr122 salary ranges
- US median$47–52/hr84 hourly ranges
- Openings
- Olive Jobs lists 185 open Data Engineering jobs in Texas and 2,600 across the US. 7% of them nationwide are listed as remote or flexible.
- Experience
- This posting asks for at least 8 years of experience; Data Engineering postings that state a minimum ask for 5 years at the median (2,267 postings).
- Education
- 1,052 of the 2,600 open Data Engineering jobs (40%) ask for a bachelor's degree or higher.
Counted across the open, public US postings on Olive Jobs on October 28, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.
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