Data Engineer - Machine Learning (Marketing Analytics)

Data Engineer- Machine Learning

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.

This role is required onsite at PODS headquarters in Clearwater, FL. The onsite working schedule is Monday - Thursday onsite with Friday remote.

It is NOT a remote opportunity.

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 & MLwith 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 Responsibilities

Direct supervisor job title(s) typically include : VP, Marketing Analytics

Job may require supervising 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) required

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)