Data Engineer

The Senior Manager, Software Engineer, Data Platform & Segmentation is an individual contributor accountable for the technical vision, design, and evolution of data platforms and segmentation capabilities that power Customer and Commercial product teams operating under a modern Product Operating Model. This role is a hands-on technical engineering role for candidates with 3 to 6 years of experience in data and machine learning engineering.

The role emphasizes deep technical expertise, product partnership, and architecture, rather than people management.

Core Accountabilities

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

• Analyze and integrate structured and unstructured data from enterprise platforms, customers, and external data providers.

• Build scalable data preparation and feature engineering pipelines for ML applications.

Machine Learning

• Develop predictive and recommendation models using appropriate statistical and machine learning techniques.

• Evaluate and select appropriate approaches based on each use case, including: Classification and regression; ranking and recommendation; clustering and segmentation; time series and forecasting; gradient boosting and tree-based models; deep learning and transformers; computer vision embeddings; vector search. and RAG

• Build reliable training and inference pipelines for batch and near-real-time use cases.

• Develop APIs and services that expose model predictions to web, mobile, CRM, Salesforce, and other enterprise applications.

• Establish rigorous model evaluation, testing, and validation practices.

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.

• Implement MLOps pipelines covering training, testing, versioning, deployment, and model lifecycle management.

• Monitor production models for model performance, data quality, drift, and other operational issues.

• Implement appropriate retraining, rollback, and model versioning strategies.

• Troubleshoot issues across data pipelines, models, inference services, APIs, and production environments.

• Create reusable ML components and patterns that can support multiple Transaction Growth use cases.

• Participate in architecture reviews, code reviews, and engineering design discussions.

Microsoft Azure Data Platform & Fabric Expertise

• 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.

Required Experience & Capabilities

• Bachelor’s degree in Computer Science, Engineering, Data Science, or equivalent experience.

• 3+ years of hands-on experience in data platform, analytics engineering, or backend engineering roles.

• Strong programming experience with Python or common data and ML libraries.

• Experience developing production machine learning models using frameworks such as scikit-learn, PyTorch, TensorFlow, XGBoost, or equivalent.

• Strong understanding of supervised and unsupervised learning, model selection, feature engineering, and statistical modeling.

• Experience preparing large datasets for machine learning, including cleansing, transformation, feature generation,n and quality validation.

• Strong SQL skills and experience working with large enterprise datasets.

• Experience designing training, evaluation,n and inference pipelines.

• Experience deploying machine learning models into production environments.

• Practical understanding of MLOps, including experiment tracking, model versioning, CI/CD, automated testing, deployment and monitoring.

• Experience developing or integrating APIs and services used for model inference.

• Strong software engineering practices including modular design, source control, code review, automated testing, ing and production debugging.

• Experience working with cloud-based data and ML platforms.

• Ability to assess multiple modeling approaches and select the simplest solution that meets the business objective.

• Strong communication skills and the ability to collaborate with product managers, business stakeholders, software engineers, and data teams

We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran, and basis of disability or any other federal, state or local protected class.