Platform Tech Lead & Architect — Remote

Cardlytics, Inc.; United States; On-site; USD 170,000 - 210,000; Full time

Benefits offered by this job

Flexible PTO

Health/vision/dental insurance begins

401(k) with company match

Employee Stock Purchase Plan

Educational assistance

Wellness stipend

Calm app subscription

Job summary

Cardlytics, Inc. is seeking a Principal Software Engineer, Tech Lead - Platform to own and evolve the data compute environment on AWS. You will shape the AI-ready foundation with a semantic layer, feature store, and data ontology while mentoring engineers and partnering with analysts and business users.

Lead architecture across batch, streaming, ML, and AI workloads, establish standards, and drive performance, reliability, and security within a fast-paced data-driven organization.

Qualifications

• 7+ years in data engineering or data platform roles with 2+ years in lead/architecture.
• Deep hands-on experience with Databricks, AWS, Delta Lake, and Spark.
• Proven ability to design lakehouse architectures and large-scale ELT/ETL pipelines.

Responsibilities

• Architect and evolve next-generation data compute architecture on AWS.
• Define platform standards and mentor engineers across data and platform teams.
• Lead governance, security, and cost governance for lakehouse platforms.
• Build AI-ready data platform including semantic layer and feature store.
• Own CI/CD for data platform deployments and enforce DevOps practices.
• Partner with analysts, engineers, and business users to reduce friction.

Skills

Databricks

AWS

Delta Lake

Unity Catalog

Spark/PySpark

ELT/ETL pipelines

Terraform

Data governance

Platform architecture

Tools

SQL

Job description

Cardlytics, Inc. is seeking a Principal Software Engineer, Tech Lead - Platform to own and evolve the data compute environment on AWS. You will shape the AI-ready foundation with a semantic layer, feature store, and data ontology while mentoring engineers and partnering with analysts and business users.

Lead architecture across batch, streaming, ML, and AI workloads, establish standards, and drive performance, reliability, and security within a fast-paced data-driven organization.