Data Engineer - MTS/SMTS/LMTS
Core
Design, build, and scale data infrastructure powering a machine learning ecosystem for real-time feature serving and model training.
Role type
Senior Data Engineer (MLOps & Feature Stores)
Builds
Scalable feature stores, streaming data pipelines, and CI/CD automation for ML workflows.
Domain
AI/ML infrastructure, Cloud Data Engineering
Deliverable
production ML models
Required skills
Python, distributed data frameworks (Airflow, Spark, Flink), feature store technologies (Feast, SageMaker, Tecton, Databricks), cloud data warehouses (Snowflake), transformation frameworks (dbt), streaming platforms (Kafka, Kinesis), infrastructure-as-code (Terraform, CloudFormation), containerization (Docker, Kubernetes), vector/graph databases, RAG pipelines
Preferred skills
Salesforce ecosystem experience, context engineering for AI systems
Technologies
Kafka, Kinesis, Flink, Airflow, Spark, Feast, SageMaker Feature Store, Tecton, Databricks Feature Store, Snowflake, dbt, Terraform, CloudFormation, Docker, Kubernetes, AWS
Responsibilities
Implement and maintain scalable features serving offline, online, and streaming ML use cases; Design and manage streaming pipelines for low-latency feature generation; Define and enforce governance standards for feature registration, metadata, lineage, and versioning; Partner with data scientists to streamline feature discovery and deployment workflows; Build and optimize large-scale data ingestion and transformation pipelines; Implement CI/CD workflows and infrastructure-as-code for feature store provisioning; Develop monitoring frameworks to track feature data quality, latency, and freshness
Seniority
Senior, hands-on IC
