Join our Data and AI team as a Staff Engineer, where you'll be instrumental in shaping the technical direction of our data pipeline and AI platforms. This role involves defining architectural strategies, guiding engineering talent, and delivering critical platform capabilities during an exciting phase of transition.

Our robust data environment leverages Redshift, MSK/RedPanda, and DBT, with ongoing development in EC2, DynamoDB, and AWS Bedrock, while phasing out Snowflake. Approximately 90% of our data is managed internally.

Key responsibilities include designing and implementing complex data pipelines across Redshift, MSK/RedPanda, DBT, and S3. You will establish high engineering standards and best practices for the Data and AI platform team. This role also involves leading technical designs for AI/ML infrastructure, including integrations with AWS Bedrock and Claude-based workflows.

Further duties encompass driving data ingestion standardization and canonical ID resolution as a company-wide initiative. You will collaborate closely with ML Engineers on data infrastructure for model training and inference pipelines, implement comprehensive data quality controls and observability across pipelines using tools like Coralogix and Grafana, and contribute to PCI DSS-compliant data handling, including PAN masking and PII controls.

Additionally, you will mentor Lead and Senior Engineers within the team, fostering their growth and technical development.

We are seeking a Staff Engineer with 8–10 years of experience in data engineering or AI platform engineering. Essential expertise includes advanced proficiency with AWS data services such as EKS, ECS, Lambda, S3, and EC2.

Candidates must possess strong hands-on experience with Redshift and the medallion/Bronze-Silver-Gold architecture, alongside a solid understanding of DBT for large-scale data transformation. Familiarity with SQL Server CDC-based ingestion pipelines and event streaming platforms like Confluent, RedPanda, or Kafka is crucial.

Proven experience in building ML-ready data pipelines and feature engineering infrastructure, coupled with familiarity with AWS Bedrock and GenAI pipeline patterns, is required. A strong grasp of PCI DSS data handling requirements and a demonstrated ability to set technical direction and mentor engineers are also vital.