Cardlytics (NASDAQ: CDLX) is the industry-leading purchase intelligence and incentives platform. They make commerce smarter and more rewarding for everyone by helping businesses attract, understand, and incentivize consumers through their banks’ digital reward programs. Remember that time you got cash back on a cup of coffee through your banking app? That was Cardlytics!

Bridg, a division of Cardlytics, is an enterprise data and audience platform that powers the growth of Retail Media. Using an exclusive offline identity resolution capabilities along with clients’ point of sale (POS) data, they help Retailers significantly expand their 1st party data (identifying unknown customers and enriching the understanding of loyalty members), ensure reporting transparency, and create new monetization opportunities working with their Consumer Packaged Goods (CPG) partners. CPG brands and advertisers in turn gain access to verified shopper data across loyalty & non-loyalty customers that powers advanced insights, precision targeting, and closed loop measurement. Join Bridg at the cutting edge as they transform how companies connect with their customers through data.

Bridg is seeking a Principal Engineer who will be designing and implementing a highly scalable data integration and transformation platform processing a high volume of data under a defined SLA. You will be creating and building the platform that includes ingestion and transformation of data, data governance, machine learning, analytics, and consumer insights.

This Principal Engineer position reports to the Head of Engineering.

Responsibilities

• Design and implement data processing in Snowflake built for high performance and scale.
• Implement robust ETL pipelines in Python (and/or Java) with full test coverage.
• Author SQL and Snowflake stored procedures optimized for concurrency and cost.
• Automate infrastructure with Terraform and codify CI/CD pipelines (Jenkins, GitHub Actions).
• Mentor senior and mid-level engineers in design reviews, code reviews, and architectural patterns.
• Partner closely with data scientists to productionize ML models.
• Evangelize engineering best practices (Agile ceremonies, retrospectives, documentation).

Minimum Qualifications

• BA/BS in Computer Science, Engineering, or a related field.
• 5+ years building high-performance, scalable Big Data systems.
• 3+ years designing and operating production workloads in Snowflake.
• Strong proficiency in Python and SQL, including optimization techniques.
• Hands-on AWS experience (EC2, S3, Lambda, EMR, RDS, Redshift).
• Excellent communication skills, with a history of mentoring engineers and influencing cross-functional stakeholders.

Preffered Qualifications

• MS in Computer Science, Engineering, or a related field.
• Experience with streaming frameworks (AWS Kinesis, Kafka, Spark Streaming, Flink).
• Familiarity with search and analytics engines (Elasticsearch, OpenSearch).
• Expertise in ETL workflow management.
• Infrastructure as Code (Terraform) and container orchestration (EKS, ECS, Kubernetes).
• Strong grasp of data governance, cataloging, and metadata management.