P
Senior Databricks AI Engineer
PowerPlan, Inc.
1 hour ago
Full-time
On-site
Smyrna, Georgia, United States
The Senior AI/ML Engineer will modernize and scale the company’s enterprise data and AI platform by designing AI-ready data models, operationalizing ML systems, and enabling natural-language analytics through Databricks Genie or equivalent AI tooling.
This role exists to shift the organization from dashboard-driven analytics to AI-powered decision intelligence at enterprise scale.
Performance Objectives Modernize and Operationalize the Analytics Data Platform Within 6–9 months, design and implement a scalable medallion-based architecture (Bronze/Silver/Gold) in Databricks or Snowflake that supports AI-ready datasets, improves query performance by ≥30%, and reduces data reliability incidents by ≥40%.
Subtasks Redesign analytical data models for AI/ML consumption Implement governance using Unity Catalog or Snowflake controls Establish monitoring and quality validation checkpoints
Within 6 months, establish semantic models and metadata standards that enable business-facing AI querying with ≥95% data trust rating from stakeholders.
Subtasks Standardize schema design for ML and GenAI workloads Align business definitions with governed datasets Implement lineage and access controls Reduce duplicate or conflicting metric definitions
Build and Deploy Production-Grade ML Pipelines Subtasks Standardize MLflow/Feature Store workflows Implement CI/CD for ML Improve model observability and drift monitoring
Within 6 months, deploy and optimize Databricks Genie (or equivalent AI query interface) enabling business users to generate accurate plain-language insights with ≥80% adoption across target user groups.
Subtasks Improve response accuracy through model + metadata tuning Partner with Product on use-case prioritization Track and improve AI query accuracy and user engagement
Democratize AI Across Business Teams Within 12 months, embed AI-driven analytics into at least 3 core business workflows, demonstrating measurable business impact (e.g., cost reduction, revenue lift, or decision cycle time improvement).
Subtasks Identify high-value AI use cases Collaborate cross-functionally Deliver production-ready AI solutions Document business ROI outcomes
Within 12 months, define and institutionalize architectural standards, best practices, and governance frameworks adopted across Engineering and Analytics teams.
Subtasks Publish architecture reference patterns Influence long-term AI strategy
Success Metrics Summary 30%+ performance improvement in analytics workloads 40%+ reduction in data quality incidents 50% reduction in ML deployment cycle time 3+ AI use cases with measurable ROI ≥95% stakeholder trust in AI-generated insights
This is a high-impact platform leadership role enabling enterprise AI transformation. The individual will shape architecture standards, influence executive AI strategy, and lead the shift from traditional BI to AI-powered decision intelligence.
Required Qualifications 10+ years of experience in
Data Analytics ,
Data Engineering ,
ML Engineering , or
AI Engineering Strong hands-on experience with
Databricks
or
Snowflake
in production environments Expertise in
SQL ,
Python , and distributed data processing (Spark preferred) Strong understanding of
data modeling for analytics and AI Experience building and deploying
ML models
in real-world systems Familiarity with
LLMs, GenAI concepts, and AI-assisted analytics Experience with ML lifecycle tools (MLflow, Feature Stores, CI/CD for ML)
Preferred Qualifications Direct experience with
Databricks Genie
or AI-powered BI tools Experience with
Unity Catalog, Delta Live Tables , or Snowflake governance features Exposure to
Azure, AWS, or GCP
cloud platforms Experience working in regulated or enterprise SaaS environments Ability to explain complex technical concepts to non-technical stakeholders
What Success Looks Like in This Role Business users can ask questions in plain English and get
trusted, accurate insights Data models are
AI-ready, scalable, and well-governed ML models move smoothly from experimentation to production Databricks Genie adoption grows with measurable business impact AI is embedded into analytics not bolted on
Why Join Us Work on
real AI/ML problems at enterprise scale Influence the evolution of a modern
data + AI platform Partner with senior leaders shaping the company’s AI-first future Build systems that turn data into decisions not dashboards
PowerPlan is an EOE #J-18808-Ljbffr
This role exists to shift the organization from dashboard-driven analytics to AI-powered decision intelligence at enterprise scale.
Performance Objectives Modernize and Operationalize the Analytics Data Platform Within 6–9 months, design and implement a scalable medallion-based architecture (Bronze/Silver/Gold) in Databricks or Snowflake that supports AI-ready datasets, improves query performance by ≥30%, and reduces data reliability incidents by ≥40%.
Subtasks Redesign analytical data models for AI/ML consumption Implement governance using Unity Catalog or Snowflake controls Establish monitoring and quality validation checkpoints
Within 6 months, establish semantic models and metadata standards that enable business-facing AI querying with ≥95% data trust rating from stakeholders.
Subtasks Standardize schema design for ML and GenAI workloads Align business definitions with governed datasets Implement lineage and access controls Reduce duplicate or conflicting metric definitions
Build and Deploy Production-Grade ML Pipelines Subtasks Standardize MLflow/Feature Store workflows Implement CI/CD for ML Improve model observability and drift monitoring
Within 6 months, deploy and optimize Databricks Genie (or equivalent AI query interface) enabling business users to generate accurate plain-language insights with ≥80% adoption across target user groups.
Subtasks Improve response accuracy through model + metadata tuning Partner with Product on use-case prioritization Track and improve AI query accuracy and user engagement
Democratize AI Across Business Teams Within 12 months, embed AI-driven analytics into at least 3 core business workflows, demonstrating measurable business impact (e.g., cost reduction, revenue lift, or decision cycle time improvement).
Subtasks Identify high-value AI use cases Collaborate cross-functionally Deliver production-ready AI solutions Document business ROI outcomes
Within 12 months, define and institutionalize architectural standards, best practices, and governance frameworks adopted across Engineering and Analytics teams.
Subtasks Publish architecture reference patterns Influence long-term AI strategy
Success Metrics Summary 30%+ performance improvement in analytics workloads 40%+ reduction in data quality incidents 50% reduction in ML deployment cycle time 3+ AI use cases with measurable ROI ≥95% stakeholder trust in AI-generated insights
This is a high-impact platform leadership role enabling enterprise AI transformation. The individual will shape architecture standards, influence executive AI strategy, and lead the shift from traditional BI to AI-powered decision intelligence.
Required Qualifications 10+ years of experience in
Data Analytics ,
Data Engineering ,
ML Engineering , or
AI Engineering Strong hands-on experience with
Databricks
or
Snowflake
in production environments Expertise in
SQL ,
Python , and distributed data processing (Spark preferred) Strong understanding of
data modeling for analytics and AI Experience building and deploying
ML models
in real-world systems Familiarity with
LLMs, GenAI concepts, and AI-assisted analytics Experience with ML lifecycle tools (MLflow, Feature Stores, CI/CD for ML)
Preferred Qualifications Direct experience with
Databricks Genie
or AI-powered BI tools Experience with
Unity Catalog, Delta Live Tables , or Snowflake governance features Exposure to
Azure, AWS, or GCP
cloud platforms Experience working in regulated or enterprise SaaS environments Ability to explain complex technical concepts to non-technical stakeholders
What Success Looks Like in This Role Business users can ask questions in plain English and get
trusted, accurate insights Data models are
AI-ready, scalable, and well-governed ML models move smoothly from experimentation to production Databricks Genie adoption grows with measurable business impact AI is embedded into analytics not bolted on
Why Join Us Work on
real AI/ML problems at enterprise scale Influence the evolution of a modern
data + AI platform Partner with senior leaders shaping the company’s AI-first future Build systems that turn data into decisions not dashboards
PowerPlan is an EOE #J-18808-Ljbffr