Senior ML Data Engineer: Feature Pipelines & MLOps

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

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.