← Serch more jobs

Senior Databricks AI Engineer

PowerPlan, Inc. • Atlanta, GA

Not Applicable Posted March 30, 2026 Job link

Thinking about this job

Responsibilities

Commitments

Responsibilities

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.
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%.
Redesign analytical data models for AI/ML consumption
Implement governance using Unity Catalog or Snowflake controls
Optimize distributed compute performance
Establish monitoring and quality validation checkpoints
Enable AI-Ready Data Modeling & Governance
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
Within 9-12 months, implement reusable ML lifecycle pipelines (experimentation → training → evaluation→ deployment) that reduce time-to-production for ML models by ≥50%.
Subtasks
Standardize MLflow/Feature Store workflows
Implement CI/CD for ML
Improve model observability and drift monitoring
Establish model documentation standards
Implement Natural Language AI Analytics (Databricks Genie Enablement)
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
Translate business questions into semantic AI-ready datasets
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
Establish Enterprise AI Platform Standards
Within 12 months, define and institutionalize architectural standards, best practices, and governance frameworks adopted across Engineering and Analytics teams.
Subtasks
Publish architecture reference patterns
Lead design reviews
Mentor engineers
Influence long-term AI strategy

Not Met Priorities

What still needs stronger evidence

Requirements

Within 6 months, establish semantic models and metadata standards that enable business-facing AI querying with ≥95% data trust rating from stakeholders.
Implement Natural Language AI Analytics (Databricks Genie Enablement)
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.
Publish architecture reference patterns
≥95% stakeholder trust in AI-generated insights
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 Skills

Implement Natural Language AI Analytics (Databricks Genie Enablement)
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.
Experience with ML lifecycle tools (MLflow, Feature Stores, CI/CD for ML)
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
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

Education

(Not required) – Direct experience with Databricks Genie or AI-powered BI tools
(Not required) – Exposure to Azure, AWS, or GCP cloud platforms

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
Optimize distributed compute performance
Establish monitoring and quality validation checkpoints

Enable AI-Ready Data Modeling & Governance
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
Within 9-12 months, implement reusable ML lifecycle pipelines (experimentation → training → evaluation→ deployment) that reduce time-to-production for ML models by ≥50%.
Subtasks

Standardize MLflow/Feature Store workflows
Implement CI/CD for ML
Improve model observability and drift monitoring
Establish model documentation standards

Implement Natural Language AI Analytics (Databricks Genie Enablement)
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

Translate business questions into semantic AI-ready datasets
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

Establish Enterprise AI Platform Standards
Within 12 months, define and institutionalize architectural standards, best practices, and governance frameworks adopted across Engineering and Analytics teams.
Subtasks

Publish architecture reference patterns
Lead design reviews
Mentor engineers
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
80% Genie adoption in target group
3+ AI use cases with measurable ROI
≥95% stakeholder trust in AI-generated insights

Growth & Career Move
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
Applicant Privacy Notice
Please note that this is a hybrid role that involves a combination of onsite work from our corporate office as well as work from home. While we strive to accommodate flexible working arrangements when sensible, there will be times when onsite work is required. This could include scheduled office days, team meetings, client meetings, or special events.