DEPLOY
Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is a Data Engineer contract position focused on Data Strategy & AI Enablement. It offers remote work, requires strong SQL and Python skills, experience with Databricks, and familiarity with financial services data. Contract length and pay rate are unspecified.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
March 3, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Remote
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Atlanta Metropolitan Area
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🧠 - Skills detailed
#"ETL (Extract #Transform #Load)" #Delta Lake #ML (Machine Learning) #Data Extraction #AI (Artificial Intelligence) #API (Application Programming Interface) #Python #Databricks #BI (Business Intelligence) #Data Strategy #Microsoft Power BI #Datasets #Spark SQL #Data Engineering #Documentation #Data Pipeline #Data Profiling #Security #Data Quality #Spark (Apache Spark) #Strategy #SQL (Structured Query Language)
Role description
Data Engineer (Contract)
Engagement: Data Strategy & AI Enablement
Location: Remote with access to client's secure environment (VDI-based)
About the Engagement
Meaningful AI is delivering a phased data strategy and AI enablement engagement for a client. The client has 30+ datasets in Databricks. We're building an AI-driven data platform — from data discovery through canonical modeling to an analytics-ready platform.
Phase 1 is already underway, where we are focused on discovery: profiling datasets, documenting schemas, mapping entity relationships, and producing a strategic data roadmap.
Phases 2-4 build on that foundation with ingestion frameworks, workflow integration, and ML pipelines.
What You'll Do
- Profile all 30+ datasets in Databricks: table structures, row counts, data types, distributions, refresh patterns
- Document schemas with inferred relationships and primary/foreign key candidates
- Assess data quality across dimensions: completeness, consistency, accuracy, freshness
- Analyze historical data behavior — determine which datasets use snapshot vs. overwrite patterns
- Support API and integration mapping (test data extraction capabilities)
- Build standardized ingestion framework and data pipelines (Phase 2)
- Implement data quality gates with automated validation and alerting (Phase 2)
- Support workflow integration, feature engineering pipelines, and ML data products (Phases 3-4)
What We're Looking For
Required:
- Strong SQL and Python skills
- Experience with Databricks (notebooks, Spark SQL, Delta Lake)
- Hands-on data profiling, data quality assessment, and technical documentation
- ETL/ELT pipeline development experience
- Comfort working in locked-down enterprise environments with restricted internet access
- Comfort with undocumented, messy data — you'll be making sense of datasets that have limited or no documentation
- Eager to learn AI tooling
Strongly Preferred:
- Financial services, lending, or banking data experience
- Experience with Medallion Architecture (bronze/silver/gold patterns)
- Familiarity with Power BI as a downstream consumer
4
- Experience working within VDI-based access environments
- Experience with modern AI tool sets
Environment
The client's environment is managed with strict security controls. Access is through VDI (Windows) RDP into a dedicated server Databricks. Internet access on work servers is limited. You must be comfortable working within these constraints.
Data Engineer (Contract)
Engagement: Data Strategy & AI Enablement
Location: Remote with access to client's secure environment (VDI-based)
About the Engagement
Meaningful AI is delivering a phased data strategy and AI enablement engagement for a client. The client has 30+ datasets in Databricks. We're building an AI-driven data platform — from data discovery through canonical modeling to an analytics-ready platform.
Phase 1 is already underway, where we are focused on discovery: profiling datasets, documenting schemas, mapping entity relationships, and producing a strategic data roadmap.
Phases 2-4 build on that foundation with ingestion frameworks, workflow integration, and ML pipelines.
What You'll Do
- Profile all 30+ datasets in Databricks: table structures, row counts, data types, distributions, refresh patterns
- Document schemas with inferred relationships and primary/foreign key candidates
- Assess data quality across dimensions: completeness, consistency, accuracy, freshness
- Analyze historical data behavior — determine which datasets use snapshot vs. overwrite patterns
- Support API and integration mapping (test data extraction capabilities)
- Build standardized ingestion framework and data pipelines (Phase 2)
- Implement data quality gates with automated validation and alerting (Phase 2)
- Support workflow integration, feature engineering pipelines, and ML data products (Phases 3-4)
What We're Looking For
Required:
- Strong SQL and Python skills
- Experience with Databricks (notebooks, Spark SQL, Delta Lake)
- Hands-on data profiling, data quality assessment, and technical documentation
- ETL/ELT pipeline development experience
- Comfort working in locked-down enterprise environments with restricted internet access
- Comfort with undocumented, messy data — you'll be making sense of datasets that have limited or no documentation
- Eager to learn AI tooling
Strongly Preferred:
- Financial services, lending, or banking data experience
- Experience with Medallion Architecture (bronze/silver/gold patterns)
- Familiarity with Power BI as a downstream consumer
4
- Experience working within VDI-based access environments
- Experience with modern AI tool sets
Environment
The client's environment is managed with strict security controls. Access is through VDI (Windows) RDP into a dedicated server Databricks. Internet access on work servers is limited. You must be comfortable working within these constraints.
