Senior Databricks AI Engineer

PowerPlan, Inc.; Smyrna (GA); On-site; USD 120,000 - 150,000; Full time

Benefits offered by this job

Work on real AI/ML problems

Influence the evolution of a data + AI platform

Partnership with senior leaders

Job summary

A leading enterprise data solutions provider located in Smyrna, Georgia is seeking a Senior AI/ML Engineer to modernize and scale their data and AI platform. This role involves designing AI-ready data models, deploying machine learning systems, and operationalizing analytics to enhance decision-making across business workflows. The ideal candidate will bring over 10 years of hands-on experience in data analytics and engineering, particularly with tools like Databricks and Snowflake, along with a robust understanding of SQL and Python. Join a dynamic team to influence the company's AI-driven future.

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).

Responsibilities

• Modernize and operationalize the analytics data platform.
• Build and deploy production-grade ML pipelines.
• Democratize AI across business teams.

Skills

Data Analytics

Data Engineering

ML Engineering

AI Engineering

Databricks

Snowflake

SQL

Python

Distributed Data Processing

Data Modeling for Analytics

ML Models Development

ML Lifecycle Tools

GenAI Concepts

AI-assisted Analytics

Tools

Databricks Genie

MLflow

Feature Stores

CI/CD for ML

Job description

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