Engineer II - Machine Learning

PODS; Clearwater (FL); On-site; USD 120,000 - 170,000; Full time

Job summary

PODS is seeking a Data Engineer- Machine Learning to scale a modern data and AI stack, delivering measurable business impact at scale. You will design production-grade feature pipelines in Snowflake/Snowpark and Databricks, and operationalize batch and real-time inference with robust MLOps practices.

Collaborate with ED&A and Data Science to optimize models, govern data, and ensure reliable, observable ML workflows that drive revenue growth and improve customer experience.

Qualifications

• Bachelor’s or Master’s in CS, Data/ML, or related field.
• 4+ years in data/ML engineering building production-grade pipelines with Python and SQL.
• Strong hands-on with Snowflake/Snowpark and Databricks; comfort with Tasks & Streams for orchestration.
• 2+ years of experience optimizing models: batch jobs and/or real-time APIs, containerized services, CI/CD, and monitoring.
• Solid understanding of data modeling and governance/lineage practices expected by ED&A.

Responsibilities

• Design, build, and operate feature pipelines that transform curated datasets into reusable, governed feature tables.
• Productionize ML models (batch and real-time) with reliable inference jobs/APIs, SLAs, and observability.
• Setup processes in Databricks and Snowflake/Snowpark to schedule, monitor, and auto-heal training/inference pipelines.
• Collaborate with our ED&A team centered on replicating operational data into Snowflake and enabling advanced analytics & ML.
• Partner with Data Science to optimize models that grow customer base and revenue, improve CX, and optimize resources.
• Implement MLOps/LLMOps: experiment tracking, reproducible training, model/asset registry, safe rollout, and automated retraining triggers.
• Enforce data governance & security policies and contribute metadata, lineage, and definitions to the ED&A catalog.
• Optimize cost/performance across Snowflake/Snowpark and Databricks.
• Follow robust and established version control and DevOps practices.
• Create clear runbooks and documentation, and share best practices with analytics, data engineering, and product partners.

Skills

Python

SQL

Data engineering

ML engineering

Production pipelines

Education

Bachelor’s or Master’s in CS, Data/ML, or related field

Tools

Snowflake/Snowpark

Databricks

Tasks & Streams

CI/CD

Monitoring

Model registries

Feature stores

Job description

Job Summary

The Data Engineer- Machine Learning is responsible for scaling a modern data & AI stack to drive revenue growth, improve customer satisfaction, and optimize resource utilization. As an ML Data Engineer, you will bridge data engineering and ML engineering: build high‑quality feature pipelines in Snowflake/Snowpark, Databricks, productionize and operate batch/real‑time inference, and establish MLOps/LLMOps practices so models deliver measurable business impact at scale.

• Design, build, and operate feature pipelines that transform curated datasets into reusable, governed feature tables in Snowflake
• Productionize ML models (batch and real‑time) with reliable inference jobs/APIs, SLAs, and observability
• Setup processes in Databricks and Snowflake/Snowpark to schedule, monitor, and auto‑heal training/inference pipelines
• Collaborate with our Enterprise Data & Analytics (ED&A) team centered on replicating operational data into Snowflake, enriching it into governed, reusable models/feature tables, and enabling advanced analytics & ML—with Databricks as a core collaboration environment
• Partner with Data Science to optimize models that grow customer base and revenue, improve CX, and optimize resources
• Implement MLOps/LLMOps: experiment tracking, reproducible training, model/asset registry, safe rollout, and automated retraining triggers
• Enforce data governance & security policies and contribute metadata, lineage, and definitions to the ED&A catalog
• Optimize cost/performance across Snowflake/Snowpark and Databricks
• Follow robust and established version control and DevOps practices
• Create clear runbooks and documentation, and share best practices with analytics, data engineering, and product partners

Job Summary

The Data Engineer- Machine Learning is responsible for scaling a modern data & AI stack to drive revenue growth, improve customer satisfaction, and optimize resource utilization. As an ML Data Engineer, you will bridge data engineering and ML engineering: build high‑quality feature pipelines in Snowflake/Snowpark, Databricks, productionize and operate batch/real‑time inference, and establish MLOps/LLMOps practices so models deliver measurable business impact at scale.

Essential Duties And Responsibilities

• Design, build, and operate feature pipelines that transform curated datasets into reusable, governed feature tables in Snowflake
• Productionize ML models (batch and real‑time) with reliable inference jobs/APIs, SLAs, and observability
• Setup processes in Databricks and Snowflake/Snowpark to schedule, monitor, and auto‑heal training/inference pipelines
• Collaborate with our Enterprise Data & Analytics (ED&A) team centered on replicating operational data into Snowflake, enriching it into governed, reusable models/feature tables, and enabling advanced analytics & ML—with Databricks as a core collaboration environment
• Partner with Data Science to optimize models that grow customer base and revenue, improve CX, and optimize resources
• Implement MLOps/LLMOps: experiment tracking, reproducible training, model/asset registry, safe rollout, and automated retraining triggers
• Enforce data governance & security policies and contribute metadata, lineage, and definitions to the ED&A catalog
• Optimize cost/performance across Snowflake/Snowpark and Databricks
• Follow robust and established version control and DevOps practices
• Create clear runbooks and documentation, and share best practices with analytics, data engineering, and product partners

MANAGEMENT & SUPERVISORY RESPONSIBILTIES

• Direct supervisor job title(s) typically include: VP, Marketing Analytics
• Job may require managing Analytics associates

JOB QUALIFICATIONS: Essential Skills, Abilities, and Example Behavior(s)

DELIVER QUALITY RESULTS: Able to deliver top quality service to all customers (internal and external); Able to ensure all details are covered and adhere to company policies; Able to strive to do things right the first time; Able to meet agreed‑upon commitments or advises customer when deadlines are jeopardized; Able to define high standards for quality and evaluate products, services, and own performance against those standards

TAKE INITIATIVE: Able to exhibit tendencies to be self‑starting and not wait for signals; Able to be proactive and demonstrate readiness and ability to initiate action; Able to take action beyond what is required and volunteers to take on new assignments; Able to complete assignments independently without constant supervision

BE INNOVATIVE / CREATIVE: Able to examine the status quo and consistently look for better ways of doing things; Able to recommend changes based on analyzed needs; Able to develop proper solutions and identify opportunities

BE PROFESSIONAL: Able to project a positive, professional image with both internal and external business contacts; Able to create a positive first impression; Able to gain respect and trust of others through personal image and demeanor

ADVANCED COMPUTER USER: Able to use required software applications to produce correspondence, reports, presentations, electronic communication, and complex spreadsheets including formulas and macros and/or databases. Able to operate general office equipment including company telephone system

JOB QUALIFICATIONS: Education & Experience Requirements

• Bachelor’s or Master’s in CS, Data/ML, or related field (or equivalent experience)
• 4+ years in data/ML engineering building production‑grade pipelines with Python and SQL
• Strong hands‑on with Snowflake/Snowpark and Databricks; comfort with Tasks & Streams for orchestration
• 2+ years of experience optimizing models: batch jobs and/or real‑time APIs, containerized services, CI/CD, and monitoring
• Solid understanding of data modeling and governance/lineage practices expected by ED&A

Preferred Qualifications

• Familiarity with LLMOps patterns for generative AI applications
• Experience with NLP, call center data, and voice analytics
• Exposure to feature stores, model registries, canary/shadow deploys, and A/B testing frameworks
• Marketing analytics domain familiarity (lead scoring, propensity, LTV, routing/prioritization)