Principal Software Engineer, Tech Lead - Platform

About Cardlytics

Founded in 2008, Cardlytics (NASDAQ: CDLX) is the industry-leading purchase intelligence and incentives platform. We make commerce smarter and more rewarding for everyone by helping businesses attract, understand, and incentivize consumers through our partners' digital reward programs. Join us on our mission to make commerce smarter and more rewarding for everyone!

At Cardlytics, our goal is to streamline data consumption across the entire organization. We understand that accessible, reliable data underpins informed decision‑making and innovative problem‑solving. By partnering closely with product and engineering teams, we tailor our solutions to fit their specific workflow needs and make strategic investments in our platform to ensure swift access to data. Our approach is designed to simplify the way teams interact with data, to accelerate time‑to‑insight to enable Cardlytics teams to harness the full potential of their data for impactful outcomes, and to make deliberate technology choices that keep our data infrastructure cost‑effective.

Cardlytics is seeking a Principal Software Engineer, Tech Lead — Platform to join the team, reporting to the VP of Engineering, Cloud & Data Infrastructure. We are looking for an experienced technical leader to own and evolve our data compute environment end‑to‑end on AWS, including defining the next‑generation platform direction.

• Architecture and design:
  Design and evolve the Cardlytics next‑generation architecture; establish reference architectures and standards for batch, streaming, ML, and AI/agentic workloads.
• AI‑ready data platform:
  Build out the semantic layer, feature store, and data ontology that make Cardlytics data consumable by AI/agentic applications; enable governed, self‑service access for both human and machine (LLM/agent) consumers.
• User enablement:
  Serve as the primary technical partner for analysts, engineers, and business users; develop self‑service guides, onboarding materials, and best‑practice documentation to reduce friction across all user groups.
• Technical leadership:
  Set technical direction for the platform and mentor engineers across the data and platform teams; drive engineering excellence and best practices, and manage the vendor relationship.
• Performance and reliability:
  Define monitoring standards and SLAs, lead performance tuning for Spark jobs, open table formats (e.g. Delta, Hudi), and SQL query engines; own incident response and root‑cause analysis.
• Governance and security:
  Drive adoption of a unified governance layer (e.g., Databricks Unity Catalog) for centralized RBAC, column‑level security, data lineage, and metadata management; implement and maintain security best practices including encryption and audit logging.
• Platform standards and governance:
  Define the operating model and guardrails for the managed lakehouse platforms (including Databricks on AWS) — compute policies, access model, catalog structure, and cost governance — with day‑to‑day administration executed by the platform and domain teams working within those standards.
• Infrastructure as code:
  Own the CI/CD strategy for lakehouse and data platform deployments (Terraform, Git Hub Actions, and platform‑native packaging such as Asset Bundles); enforce Dev Ops practices across engineering teams.

• 7+ years of experience in data engineering or data platform roles, with at least 2 years in a technical lead or architecture capacity.
• Deep, hands‑on expertise with the leading industry platforms, frameworks, and tools:
  Databricks, AWS EMR or similar, Delta Lake, Unity Catalog, Spark (PySpark), and SQL; with a solid understanding of compute/cluster management on AWS.
• Proven experience designing and delivering Lakehouse architectures and large‑scale ELT/ETL pipelines in production environments.
• Proficiency in Infrastructure as Code tools, such as Terraform.
• Demonstrated experience supporting diverse stakeholder groups including analysts, engineers (including ML engineers), and non‑technical business users.
• Strong understanding of data governance, data modeling, and cloud security best practices.
• Excellent…