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84.51 Degrees

Data analytics for retail marketing decisions

Solution Architect

Backend EngineeringConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • A bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related technical field, or equivalent practical experience.
  • Seven or more years of experience in software engineering, data engineering, platform engineering, solution architecture, or a related technical role.
  • Three or more years of experience in a technical leadership, architecture, staff engineer, principal engineer, or solution design role.
  • Experience designing distributed systems, APIs, data platforms, or cloud applications.
  • Strong understanding of architecture patterns such as microservices, API-first design, event-driven architecture, domain-driven design, and evolutionary architecture.
  • Experience with cloud platforms, preferably Azure, and modern data platform patterns.
  • Experience building prototypes or proof-of-concept solutions to validate ideas before full-scale implementation.
  • Experience working with cross-functional teams including product, engineering, and data science stakeholders.
  • Experience with CI/CD, automated testing, monitoring, logging, and production support practices.
  • Experience with Databricks, Snowflake, Delta Lake, Azure Data Lake Storage, Spark, or similar cloud data platforms.
  • Familiarity with AI and machine learning system patterns such as model training pipelines, inference services, feature engineering, model registries, or MLOps.
  • Familiarity with large language model application patterns such as retrieval-augmented generation, semantic search, vector databases, prompt orchestration, or AI agents.
  • Experience with managed AI services such as Azure OpenAI, Databricks AI and ML capabilities, Snowflake Cortex AI, or similar platforms.
  • Experience designing REST, GraphQL, event-based, or asynchronous APIs.
  • Familiarity with API gateways, OAuth, OIDC, OpenAPI specifications, rate limiting, versioning, and service-to-service authentication.
  • Experience with governance, security, and observability concepts including role-based access control, lineage, audit logging, cost attribution, data access controls, and platform monitoring.
  • Experience in retail media, ad tech, marketing technology, customer analytics, loyalty, or large-scale consumer data environments.

Responsibilities

  • Partner with product managers, engineers, data scientists, machine learning engineers, and business stakeholders to understand problems, shape solutions, and define technical paths forward.
  • Design end-to-end solution architectures across data platforms, APIs, AI-enabled applications, internal tools, and client-facing capabilities.
  • Translate business and product needs into technical designs, implementation plans, architecture diagrams, and phased delivery roadmaps.
  • Build prototypes, proofs of concept, and reference implementations to validate technical feasibility and help teams move faster.
  • Help define practical architecture patterns for cloud applications, APIs, data pipelines, semantic layers, AI and machine learning workflows, and event-driven integrations.
  • Work with engineering teams to make key technical decisions around system design, integration patterns, data models, platform choices, and build-versus-buy tradeoffs.
  • Help shape API strategies that make data, models, and platform capabilities easier to consume securely and consistently across teams and products.
  • Create solution design documents, API specifications, architecture decision records, reference architectures, and technical implementation guidance.
  • Embed with delivery teams when needed to clarify requirements, unblock implementation issues, validate technical feasibility, or help move a concept from prototype to production.
  • Partner with security, data governance, and platform teams to ensure solutions are secure, compliant, observable, and supportable.
  • Mentor engineers and technical teams on architecture practices, API design, data architecture, AI system design, and pragmatic delivery.
  • Evaluate emerging technologies, including managed AI platforms, semantic layer tools, agent frameworks, and cloud data services, with a focus on practical adoption.

About the Company

84.51 Degrees

Data analytics for retail marketing decisions

84.51 Degrees analyzes large volumes of consumer data to help retailers and consumer goods companies understand shopper behavior and tailor marketing strategies. Its main product is data analytics and predictive analytics tools that uncover consumer insights, trends, and preferences across generations. These insights are delivered to B2B clients via subscriptions, project-based work, or performance-based pricing, enabling clients to make data-driven decisions and optimize customer engagement across the purchasing journey. The company differentiates itself through a strong emphasis on culture and inclusive workplace values, which it views as important for driving collaboration and innovation. Overall, the goal is to help clients stay relevant and competitive by translating data into actionable marketing and merchandising strategies.

Company size
1,001-5,000
Company stage
M&A
Total funding
$5.5M
Headquarters
Cincinnati, OH
Founded
2015

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