You will get production-ready dbt models and pipelines on Databricks Lakehouse
Project details
You'll get clean, production-ready dbt models and pipelines on Databricks Lakehouse, built with a proper staging -> intermediate -> fact/mart architecture, tests, and documentation. I specialize in migrating legacy SAS and SQL workloads to modern dbt + Databricks stacks using Unity Catalog, Delta Lake, and GitHub-based CI/CD. You get readable, well-tested models your team can maintain long after I'm done, not just a one-off script dump.
Database Type
MS SQL, Oracle, PostgreSQLWhat's included
| Service Tiers |
Starter
$50
|
Standard
$150
|
Advanced
$300
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
Number of Tables Added | 3 | 8 | 15 |
Schema Diagram | - | ||
Permissions Setup | - | - | - |
Import/Export Data | - | - | |
Admin Panel Setup | - | - | - |
Frequently asked questions
About Vantipenta
Data Engineer | Databricks, dbt, SQL | SAS-to-dbt GenAI Migration
Hyderabad, India - 4:57 pm local time
What I've delivered recently:
• Migrated legacy SQL Server finance archives to Databricks Lakehouse — reconciled 106.9M+ journal records and 2.4M+ payables records with zero-difference checks and stakeholder sign-off
• Built dbt models (staging → intermediate → fact) for tax reporting, released via GitHub PRs and CI/CD into SIT and Prod-Eng environments
• Designed a GenAI-powered SAS-to-dbt migration framework — conversion prompts and validation matrices achieving ~98–100% transformation-logic accuracy and 100% required-column alignment (est. 5,000 hours saved per 1,000 scripts)
• Set up Lakehouse environments end to end: Unity Catalog, ADLS external locations, storage credentials, schema grants, service-principal/run-as configuration
• Authored onboarding and governance artefacts that get engineering teams productive on Databricks + dbt fast
What I can do for you:
• Databricks/dbt pipeline builds and migrations (SQL Server, Oracle, SAS → Lakehouse)
• Dimensional modeling — staging/fact layers, incremental loads, windowing and ranking logic
• Large-scale data validation and reconciliation
• AI-assisted code conversion and automation (prompt engineering with LLMs)
• Python & SQL scripting, GitHub workflows, deployment governance
Earlier background: 3 years of Oracle SQL/PLSQL development in insurance.
I document everything, communicate proactively, and treat reconciliation as non-negotiable — numbers must match before sign-off. Available 30+ hrs/week.
Steps for completing your project
After purchasing the project, send requirements so Vantipenta can start the project.
Delivery time starts when Vantipenta receives requirements from you.
Vantipenta works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & access setup
Review your requirements, get access to source code/data and your Databricks workspace, and map out the staging -> intermediate -> fact model plan.
Build & test dbt models
Build staging, intermediate, and fact/mart models with dbt tests, then walk through the models with you and incorporate feedback.