**About Cardlytics**
Remember that time you got cash back on a cup of coffee through your banking
app? That was us!
Cardlytics (NASDAQ: CDLX) is the industry-leading purchase intelligence and
incentives platform. We are a product-driven company that cares about three
things: our people, our customers, and our partners. Together, we make
commerce more rewarding for everyone by helping businesses attract,
understand, and incentivize consumers through their banks’ digital channels.
* _Location: *_ Remote - PST Time Zone
**About the Team**
At Data Infra, our goal is to streamline Cardlytics data consumption across
the entire organization. We understand that accessible, reliable data
underpins informed decision-making and innovative problem-solving. By
partnering closely with teams, we tailor our solutions to fit their specific
workflow needs and make strategic investments in our Data Infra platform to
ensure swift access to data. Our approach is designed to not only simplify the
way teams interact with data but also to accelerate time-to-insight, enabling
Cardlytics teams to harness the full potential of their data for impactful
outcomes.
**About the Position**
Cardlytics is seeking a Senior Principal Engineer, Data Infra to join our
team, reporting to the Director Cloud & Data Infra.
As part of our growing team, you'll have the opportunity to work closely with
cross-functional partners across the organization, leveraging your expertise
to build innovative solutions that drive business impact. By joining us,
you'll be at the forefront of shaping Cardlytics' data strategy and
contributing to a culture of collaboration, experimentation, and continuous
learning.
**Responsibilities:**
* Cross-Functional Collaboration: Work closely with both data science and analytics functions to understand their current processes, identify workflow inefficiencies, and pinpoint areas where Data Infra investments could enhance self-service capabilities within Cardlytics.
* Infrastructure Audit & Assessment: Conduct a comprehensive audit of existing Data Infra, with a focus on identifying gaps that hinder the creation of self-service data environments for analytics teams and evaluating opportunities to improve infrastructure components like storage, processing power, and access mechanisms.
* Proof of Concept (PoC) Development & Evaluation: Design, execute, and evaluate PoCs aimed at addressing identified inefficiencies within the data science and analytics workflows, ensuring that these solutions are scalable and can be effectively rolled out across the organization.
* Technology Assessment: Research and assess technology vendors to determine the most suitable infrastructure upgrades or services that support self-service data needs and facilitate creation of derivative datasets.
* Infrastructure Optimization & Maintenance: Oversee the performance of Data Infra, making continuous improvements to ensure speed, scalability, reliability, security, and efficiency in serving both data science and analytics teams' requirements.
* Process Improvement & Documentation: Streamline existing processes related to data access, model deployment, and dataset creation by implementing automation solutions where appropriate and maintaining comprehensive documentation for ease of understanding and adoption.
* Change Management & Training, Innovation & Future Planning: Lead the transition to new infrastructure components, providing training and support to both analytics teams and software developers. Keep abreast of emerging trends in data infrastructure and MLOps to drive innovation within Data Infra, ensuring that Cardlytics remains at the forefront of data-driven decision-making.
**Minimum Qualifications**
* 9+ years of experience in a related field
* Experience with Self-Service Data Organizations: A solid understanding of self-service data environments, including the challenges and best practices for catering to diverse user groups within an organization. Familiarity with tools and platforms that enable non-technical users to access and analyze data independently.
* Data Infrastructure Landscape: In-depth knowledge of modern data infrastructure components, including cloud services, databases (SQL/NoSQL), big data processing frameworks (Spark, Trino or similar), and data management architectures (Hudi, Iceberg or similar).
* Building Data Infrastructure Systems: Proven experience in designing, implementing, and managing robust data infrastructure systems that support the needs of both analytics and machine learning workflows.
* Cross-Functional Collaboration: Ability to effectively collaborate with cross-functional teams, including data scientists, analysts, developers, and IT professionals, to ensure alignment between technical capabilities and business requirements.
* Hands-On Technical Expertise: Strong hands-on experience with the tools and technologies used for building and scaling data infrastructure systems, including SQL/NoSQL databases, ETL processes, data warehousing solutions, and cloud services platforms like AWS, GCP, or Azure.
* Agile & Adaptive Mindset: Able to work in an agile environment, adapting to new technologies and methodologies quickly while maintaining a focus on delivering high-quality results under tight deadlines.
* Leadership & Influence: Capable of leading initiatives and influencing decision-making within the organization to drive the adoption of new technologies and practices that enhance data capabilities across all user groups.
* Machine Learning Operations (MLOps): Strong background in MLOps practices, with experience in automating the end-to-end lifecycle of machine learning models—from data preparation and model training to deployment and monitoring.
* Expertise in Cloud Data Platforms (Snowflake, Databricks or similar): Hands-on experience with these platforms, including their architecture, data storage capabilities, performance tuning, security features, and integration points with other tools in the ecosystem (e.g., Notebooks, MLflow etc.).
* Data Operations Automation: Proficiency in automating data operations to improve efficiency, reduce manual work, and ensure consistent delivery of services within the data infrastructure ecosystem.
* Bachelor's degree in Computer Science or related field
**Preferred Qualifications**
* PHD or Master’s degree preferred in Computer Science or related field
