Description

• **Product Model & Discovery Partnership**
• Partner closely with Product Managers, Designers, and Tech Leads to co-own outcomes, not just data assets.
• Participate actively in product discovery to ensure segmentation strategies are technically feasible, scalable, and analytically sound.
• Translate business and customer questions into durable data models and segmentation frameworks.
• **Data Platform & Segmentation Architecture**
• Define and evolve the **segmentation architecture** across customer and commercial data domains.
• Design scalable data models that support real-time, near real time, and batch segmentation use cases.
• Ensure segmentation logic is reusable, explainable, and consistent across channels and products.
• Make explicit trade-offs across latency, accuracy, cost, privacy, and maintainability.
• **Engineering Execution & Data Quality**
• Build and maintain high-quality, production-grade data pipelines and services.
• Ensure strong standards for data quality, lineage, observability, and reliability.
• Reduce fragmentation and duplication in segmentation logic across teams.
• Leverage metrics to continuously improve data freshness, accuracy, and usability.
• **Individual Contributor Technical Leadership**
• Act as a go-to expert for data platform and segmentation design.
• Lead complex technical initiatives end-to-end through hands-on contribution.
• Influence technical direction through design reviews, reference implementations, and documented standards.
• Mentor senior engineers and Tech Leads through coaching and technical guidance (without direct management responsibility).
• **Microsoft Azure Data Platform & Fabric Expertise**
• Demonstrate **deep, hands-on expertise** with Microsoft Azure data services and their application in large-scale, product-centric environments.
• Design and evolve segmentation and data platform architectures leveraging **Azure Data Fabric concepts**, ensuring interoperability, governance, and reuse across domains.
• Apply strong architectural judgment across core Azure data products, including data ingestion, storage, processing, analytics, and activation layers.
• Optimize designs across **cost, performance, latency, and scalability**, using Azure-native capabilities and patterns.
• Ensure secure-by-design implementations aligned with Azure identity, access, encryption, and compliance controls.
• Partner with enterprise architecture, cloud, and security teams to ensure Azure data platform decisions align with broader enterprise strategy while preserving team autonomy.
• Stay current on Azure data platform evolution and proactively assess new capabilities for business value, not novelty.
• **Business Partnership & Communication**
• Serve as a trusted technical partner to Customer and Commercial stakeholders.
• Communicate segmentation concepts, assumptions, and limitations in clear business language.
• Proactively surface data constraints, privacy considerations, and trade-offs to enable informed decisions.
• Support external partner and vendor conversations as a technical authority when needed.
• **Governance, Privacy & Compliance**
• Ensure segmentation approaches comply with data privacy, consent, and regulatory requirements.
• Collaborate with Security, Privacy, and Legal teams to embed governance into platform design—not bolt it on later.
• Advocate for responsible and ethical use of customer and commercial data.
• **Success Measures**
• Segmentation capabilities measurably improve customer engagement and commercial outcomes.
• Reduced duplication and inconsistency in segmentation logic across products.
• Improved data quality, freshness, and trustworthiness.
• Faster time-to-insight and activation for product teams.
• Platforms and models that scale with growth while controlling cost and risk.
• **Leadership Profile**
• Outcome-driven, not data for data’s sake
• Deep technical expertise with strong product intuition
• Influences through credibility and clarity, not authority
• Comfortable operating in ambiguity and evolving problem spaces
• Holds a high bar for data quality, ethics, and reliability

**Required Experience & Capabilities**
 

• Bachelor’s degree in Computer Science, Engineering, Data Science, or equivalent experience.
• 8+ years of hands-on experience in data platform, analytics engineering, or backend engineering roles.
• Deep expertise in data modeling, segmentation strategies, and large-scale data systems.
• Strong experience with cloud-native data platforms and modern data tooling.
• Proven ability to partner closely with product and business stakeholders.
• Demonstrated impact as a senior individual contributor on complex, cross-team initiatives

Key Skills

Education

Bachelor's degree