Join our Data and AI team as a Staff Engineer, where you will define and uphold technical excellence in data pipeline and AI platform engineering. This role involves leading architectural decisions, guiding fellow engineers, and delivering sophisticated platform solutions during a significant period of transition.

Our current data environment utilizes SQL Server CDC feeding into Confluent/RedPanda and then into Redshift (Bronze/Silver/Gold) via DBT. We leverage EC2, DynamoDB, and AWS Bedrock, with Snowflake being phased out. Approximately 90% of our data is internal.

Key responsibilities include designing and implementing robust data pipelines across Redshift, MSK/RedPanda, DBT, and S3. You will establish and enforce engineering standards and best practices for the Data and AI platform team.

This role also involves leading the technical design for AI/ML infrastructure, including integrations with AWS Bedrock and Claude-based workflows. You'll drive data ingestion standardization and canonical ID resolution as a company-wide initiative. Collaboration with ML Engineers on data infrastructure for model training and inference pipelines is essential.

Additional duties include implementing comprehensive data quality controls and observability across pipelines using tools like Coralogix and Grafana. You will contribute to PCI DSS-compliant data handling, including PAN masking, PII controls, and TonicAI integration. Mentoring Lead and Senior Engineers within the team is also a core aspect of this position.

We are seeking candidates with 8–10 years of experience in data engineering or AI platform engineering. Expertise in AWS data services such as EKS, ECS, Lambda, S3, and EC2 is crucial.

Strong hands-on experience with Redshift and the medallion/Bronze-Silver-Gold architecture is required, alongside proficiency in DBT for large-scale data transformation. Familiarity with SQL Server CDC-based ingestion pipelines and event streaming platforms like Confluent, RedPanda, or Kafka is expected.

Candidates should have experience building ML-ready data pipelines and feature engineering infrastructure. Knowledge of AWS Bedrock and GenAI pipeline patterns, along with a solid understanding of PCI DSS data handling requirements, is necessary. A proven ability to set technical direction and mentor engineers is also a key requirement.