Engineer II Machine Learning

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 highâquality feature pipelines in Snowflake/Snowpark Databricks productionize and operate batch/realâtime inference and establish MLOps/LLMOps practices so models deliver measurable business impact at scale.

ESSENTIAL DUTIES AND RESPONSIBILITIES

â Design build and operate feature pipelines that transform curated datasets into reusable governed feature tables in Snowflake

â Productionize ML models (batch and realâtime) with reliable inference jobs/APIs SLAs and observability

â Setup processes in Databricks and Snowflake/Snowpark to schedule monitor and autoâheal training/inference pipelines

â 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

â Partner with Data Science to optimize models that grow customer base and revenue improve CX and optimize resources

â Implement MLOps/LLMOps: experiment tracking reproducible training model/asset registry safe rollout and automated retraining triggers

â Enforce data governance & security policies and contribute metadata lineage and definitions to the ED&A catalog

â Optimize cost/performance across Snowflake/Snowpark and Databricks

â Follow robust and established version control and DevOps practices

â 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

Bachelors or Masters in CS Data/ML or related field (or equivalent experience)

4 years in data/ML engineering building productionâgrade pipelines with Python and SQL

Strong handsâon with Snowflake/Snowpark and Databricks; comfort with Tasks & Streams for orchestration

2 years of experience optimizing models: batch jobs and/or realâtime 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)

Required Experience:

IC