What you’ll bring to the table: (Responsibilities)

•    Lead the development of scalable back-end systems to support platform services.
•    Architect RESTful APIs and microservices using functional programming to ensure reliability and maintainability.
•    Build real-time data pipelines to enable efficient platform operations.
•    Optimize databases for performance and data integrity.
•    Mentor team members on functional programming practices and system design for high-quality, testable code.
•    Collaborate with DevOps and data teams to align on platform performance and operational needs.
•    Implement security measures to safeguard sensitive platform data.
•    Oversee projects from design to deployment, ensuring alignment with business goals.
•    Enhance CI/CD processes to maintain platform reliability.
•    Research advanced functional programming techniques to improve system performance.

Do you have the right mix of ingredients:

• 7+ years of experience in back-end software development, with at least 3 years focused on functional programming.
• Expert proficiency in Scala or other functional programming languages for building complex back-end systems.
• Strong experience with functional programming libraries, including Cats Effect, for concurrent and scalable applications.
• Advanced skill in designing RESTful APIs and microservices with functional paradigms.
• Proficiency in event streaming systems like Kafka for real-time processing.
• Expertise in optimizing relational databases (e.g., PostgreSQL) for high-throughput environments.
• Ability to write clean, testable code adhering to functional programming principles.
• Deep knowledge of security practices for critical systems.
• Proven ability to lead technical projects and mentor engineers across teams.
• Experience with CI/CD pipelines and containerized environments (e.g., Docker, Kubernetes).
• Familiarity with Typelevel ecosystem (preferred).
• Experience building applications using Python (preferred).

Education, Certifications and Experience: Preferred

• Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent experience (e.g., coding bootcamp or self-taught expertise).

What you’ll bring to the table: (Responsibilities)

• Develop and maintain back-end features for web applications using modern languages and frameworks. 
• Design and build scalable, maintainable system architectures, including RESTful microservices.
• Integrate and manage relational databases to ensure efficient data storage and retrieval.
• Implement event-driven and streaming solutions to support real-time data processing.
• Write high-quality, well-tested code to ensure reliability, performance, and scalability.
• Partner with cross-functional teams to deliver solutions aligned with business goals and timelines.
• Ensure security best practices are embedded across back-end systems and data flows.
• Contribute to continuous improvement of development processes, tools, and best practices.

Do you have the right mix of ingredients:

• 4+ years of back-end software development experience (7+ preferred), with a strong focus on Scala and functional programming.
• Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent experience (e.g., coding bootcamp or self-taught expertise)
• Proficiency in Scala and other JVM or functional programming languages
• Experience designing and building RESTful microservices and scalable system architectures.
• Hands-on experience with relational databases and data management best practices.
• Familiarity with event-driven systems and streaming technologies (e.g., Kafka).
• Experience with modern development tools and infrastructure, including Docker and Kubernetes (preferred).
• Strong coding practices, including writing clean, testable, and maintainable code.
• Understanding of web security principles and secure application development.
• Experience working across the full software development lifecycle, including CI/CD practices.
• Ability to collaborate effectively with cross-functional technical and non-technical teams.
• Familiarity with Lightbend, Typelevel, or similar ecosystems (preferred).

What you’ll bring to the table: (Responsibilities)

Short term specific: 
Oversee SLAs across the Intelligent Assignment Engine (IAE) pipeline (batch completion, feed delivery, attribute freshness, assignment turnaround) and drive activity-based classification and tiering of members and merchants, ensuring definitions, thresholds, and refresh cadences are aligned with business and downstream consumption needs. 

Design and build the offer priority scoring framework used to rank eligible offers per member, including score definition, input features (member attributes, merchant attributes, behavioral signals, business priorities), weighting logic, and validation against business objectives, and evolve the scoring model as personalization and ML capabilities mature.      

• Provide technical leadership and execution guidance across the IAE pipeline, including nightly batch assignment processing, member and merchant attribute management, eligibility evaluation logic, and S3-based data feed production
• Lead the design, generation, and ongoing management of customer segmentation and feature pipelines, including member group construction, attribute bucketing strategy, and the production of feature sets used for offer eligibility evaluation and targeting 
•  Lead the design and optimization of large-scale data processing workflows handling hundreds of millions to billions of records, including partitioning strategy, bulk ingestion, and performance tuning
• Own the evolution of the IAE attribute pipeline including member and merchant attribute design, metric definition, bucketing strategy, and ongoing quality validation, and drive alignment with business stakeholders on open decisions (tier boundaries, bucket values, time windows, metric design)  
• Provide architectural guidance on the development of a real-time synchronous API layer for offer     assignment, including near-real-time member enrollment and user-triggered assignment events, and     ensure sound integration with the broader batch pipeline
• Oversee the analytics mart maintenance and incremental build-out post-MVP, ensuring data models remain accurate, performant, and aligned with evolving reporting needs  
• Act as the primary technical bridge between business stakeholders (product, marketing, finance) and  the IAE/data engineering team, translating business requirements into data pipeline and attribute requirements and translating data constraints back into business-legible terms      
• Guide and review the work of junior data scientists and data engineers on the IAE team, providing technical direction and prioritization support without formal people management responsibility      
• Collaborate with the Nova platform team and engineering to ensure IAE outputs meet downstream data contracts, S3 feed schemas, and serve the offer assignment pipeline reliably      
• Define and maintain data contracts between IAE and downstream consumers (Nova, analytics marts, dashboards)
• Identify and surface data quality risks, pipeline gaps, and go-live readiness issues across IAE's attribute and assignment outputs
•  Build and maintain documentation of IAE architecture, data models, pipeline logic, attribute definitions, and feature generation processes to reduce key-person dependency and support team continuity
• Contribute to the future development of offer personalization and recommendation capabilities, including A/B testing frameworks, behavioral modeling from member dining habits, and an informed point of view on how ML and generative AI approaches can evolve the assignment and eligibility engine over time. 
  

Do you have the right mix of ingredients: (Requirements)

• Master’s degree in data science or related field
• 5+ years of experience in a data science role
• Proven ability to define and operate pipeline SLAs and data freshness guarantees, including monitoring, alerting, and incident response for batch and near-real-time data workflows.
• Demonstrated track record designing activity-based segmentation and tiering frameworks (e.g., RFM-style models, engagement tiers, merchant activity classifications), including threshold definition, refresh cadence design, and validation against business outcomes.      
• Hands-on background designing and implementing scoring, ranking, or recommendation frameworks, including feature selection, weighting strategies (rule-based, heuristic, and/or ML-driven), and evaluation against business objectives; familiarity with evolving such systems from deterministic scoring toward ML-based personalization.
• Strong technical foundation across data science and data engineering; this is not a purely analytical role and the person must be comfortable owning and directing production pipeline work
• Proven experience designing and managing customer segmentation pipelines and feature generation at scale, including the construction and lifecycle management of member groups, derived attributes, and reusable feature sets
• Experience designing and leading large-scale data processing systems (hundreds of millions to billions  of records), including batch pipeline architecture, partitioning, staging, and performance optimization
• Experience with workflow orchestration (Airflow/MWAA or equivalent) and AWS data services (S3, Glue, Aurora/PostgreSQL)      
• Strong SQL and Python skills; ability to review, guide, and produce production-quality data pipeline code      
• Understanding of event-driven architectures and Kafka-based data replication patterns      
• Experience with or strong understanding of real-time or near-real-time data systems and ability to provide architectural guidance even if not the primary builder
• Ability to design, define, and document data attributes, metrics, and eligibility logic from business  requirements
• Experience working with large-scale member or customer data in a personalization, targeting, loyalty, or recommendation context      
• Demonstrated ability to work cross-functionally and influence without authority; comfortable in stakeholder-facing conversations and capable of holding a business conversation and a technical one in the same meeting
• Self-directed and autonomous; able to own a workstream end-to-end and provide leadership direction to others with minimal oversight
• Strong written and verbal communication; able to produce clear documentation and present findings to non-technical audiences 

Nice to Have:

• Experience with offer, loyalty, dining, or hospitality platforms
• Familiarity with Scala or JVM-based systems, particularly in the context of real-time API or microservice architectures that the data pipeline integrates.
• Experience with analytics engineering (dbt or similar) or oversight of BI data model layers
• Familiarity with CDC-based replication patterns and data synchronization between systems
• Familiarity with ML model deployment and serving (AWS SageMaker, Bedrock, or equivalent), A/B testing frameworks, and an understanding of how foundation models and RAG-based architectures can be applied to personalization and recommendation use cases at scale. This is a direction the platform is moving toward and the right candidate will have an informed point of view on it. 

Tech Stack:

• Aurora (PostgreSQL) / SQL
• Python      
• AWS (S3, Glue, Redshift, CloudWatch, MSK)
• Airflow (MWAA)
• Kafka / Kafka Connect
• Scala / Kubernetes: familiarity and architectural awareness required; not expected to be the primary  builder 

What you’ll bring to the table: (Responsibilities)

• Design and own the scoring framework that ranks eligible offers per member — defining features, weighting logic, and validating against business outcomes, then evolving it from deterministic scoring toward ML-driven personalization.
• Lead segmentation and feature pipelines: member group construction, derived attributes, bucketing strategy, and reusable feature sets for eligibility evaluation and targeting.
• Architect and optimize large-scale batch processing workflows handling hundreds of millions to billions of records, including partitioning, bulk ingestion, and performance tuning.
• Define and operate SLAs across the pipeline: batch completion, feed delivery, attribute freshness, and assignment turnaround.
• Provide architectural guidance on a near-real-time assignment API layer and its integration with the broader batch pipeline.
• Define and maintain data contracts with downstream consumers (analytics marts, dashboards, adjacent platforms) and oversee the incremental build-out of analytics data models.
• Translate between business stakeholders (product, marketing, finance) and the engineering team — comfortable holding a business conversation and a technical one in the same meeting.
• Document architecture, data models, pipeline logic, and feature generation processes to reduce key-person dependency and support team continuity.
• Shape the future roadmap for personalization and recommendations, including A/B testing frameworks, behavioral modeling from member activity, and the role of ML and generative AI in assignment and eligibility.

Do you have the right mix of ingredients: (Requirements)

• Master’s degree in data science or related field
• 5+ years of experience in a data science role
• Strong technical foundation across both data science and data engineering — this role owns and directs production pipelines, not just analysis.
• Proven experience designing and leading large-scale data processing systems (hundreds of millions to billions of records), including batch architecture, partitioning, staging, and performance optimization.
• Track record designing activity-based segmentation and tiering frameworks (e.g., RFM-style models, engagement tiers, merchant activity classifications) — from threshold definition through refresh cadence and validation against business outcomes.
• Hands-on background building scoring, ranking, or recommendation frameworks, with feature selection, weighting strategies (rule-based, heuristic, or ML-driven), and evaluation against business objectives; experience evolving such systems from deterministic scoring toward ML-based personalization.
• Experience designing and managing customer segmentation pipelines and feature generation at scale, including the lifecycle management of member groups, derived attributes, and reusable feature sets.
• Experience with workflow orchestration (Airflow/MWAA or equivalent) and AWS data services (S3, Glue, Aurora/PostgreSQL).
• Strong SQL and Python skills — able to review, guide, and produce production-quality data pipeline code.
• Understanding of event-driven architectures and Kafka-based data replication patterns.
• Experience with or strong understanding of real-time or near-real-time data systems, and the ability to provide architectural guidance even when not the primary builder.
• Ability to define pipeline SLAs and data freshness guarantees, including monitoring, alerting, and incident response for batch and near-real-time workflows.
• Experience working with large-scale member or customer data in a personalization, targeting, loyalty, or recommendation context.
• Demonstrated ability to work cross-functionally and influence without authority; self-directed and able to own a workstream end-to-end with minimal oversight.
• Strong written and verbal communication; able to produce clear documentation and present findings to non-technical audiences.

Nice to Have:

• Experience with offer, loyalty, dining, or hospitality platforms.
• Familiarity with Scala or JVM-based systems, particularly in real-time API or microservice contexts that integrate with data pipelines.
• Experience with analytics engineering (dbt or similar) or oversight of BI data model layers.
• Familiarity with CDC-based replication patterns and data synchronization between systems.
• Familiarity with ML model deployment and serving (AWS SageMaker, Bedrock, or equivalent), A/B testing frameworks, and an informed point of view on how foundation models and RAG-based architectures can be applied to personalization and recommendation at scale.

Tech Stack:

• Aurora (PostgreSQL) / SQL
• Python
• AWS (S3, Glue, Redshift, CloudWatch, MSK)
• Airflow (MWAA)
• Kafka / Kafka Connect
• Scala / Kubernetes — architectural awareness required; not expected to be the primary builder