Purpose of Engagement:
This Statement of Work defines the responsibilities of the Data Products Engineering and Delivery Team. The team will design, build, deploy, and operate enterprise-scale data products including customer audiences, behavioral segments, analytical datasets, ADRs, machine learning models, APIs and secure customer-facing portals.
 
The goal is to accelerate DataCo’s ability to monetise data assets and deliver high-value, privacy-preserving insights to internal and external customers.
 
Scope of services:
The engagement covers the end-to-end development of data products, including:
 
Data Ingestion and Integration:

• Integration with downstream systems such as CRM, billing, app usage, network data, DPI, mobile financial services, and digital platforms.

 
Data modelling and feature engineering:

• Development of segmentation, audience building, feature stores, and analytical models used across DataCo products.

 
Machine learning products:

• End-to-end development of ML pipelines including training, validation, deployment, monitoring and retraining.

 
Customer and internal insight delivery:

• Development of secure portals used by enterprise client’s business units to access audiences, insights and ML outputs.

 
API services and automation:

• APIs that expose insights, predictions, scores and ADRs for consumption by enterprise systems and partner integrations.

 
Cloud enablement, devOps and platform engineering:

• Cloud infrastructure deployment, CI/CD automation and MLOps support for continuous delivery.

 
Operations and support:

• Ongoing platform stability, monitoring, incident management and L2/L3 support.

 
Workstreams and responsibilities:
Delivery and product lifecycle management:

• Oversee the complete lifecycle of data products and ML models from design to production support.
• Lead agile rituals and coordinate with DataCo, IT, OpCos and third parties.
• Prioritise product backlog items based on commercial value and customer need.
• Ensure all releases align with DataCo’s monetisation strategy.

 
Enterprise data architecture:

• Design ingestion architecture for complex data sources including telco events, CRM and digital platforms.
• Define data models, analytical layers, feature stores and integration patterns.
• Ensure designs comply with privacy and data protection regulations.

 
Data engineering and data ingestion:

• Build high-volume pipelines for data ingestion, transformation and processing.
• Develop audience builder pipelines, segmentation layers and ADR-ready datasets.
• Apply quality checks, enrichment logic and performance optimisation.

 
Machine learning engineering and modelling:

• Build and deploy ML models for churn, propensity, credit scoring, fraud detection, clustering and behavioral analytics.
• Implement pipelines for feature extraction, training, evaluation and model serving.
• Ensure model governance, fairness, explainability and lifecycle management.

 
Application engineering and customer portal development:

• Develop secure internal and external portals for insight browsing audience management and score retrieval.
• Implement authentication, authorisation, audit logging and encryption.
• Build front-end and back-end components for user-friendly data product access.

 
API and integration engineering:

• Build secure APIs for insights, scores, ADRs and ML outputs.
• Enable integration with banks, insurers, retailers, FinTech’s and internal client systems.
• Implement monitoring, rate limiting and usage analytics.

 
Cloud Engineering, DevOps and MLOps:

• Deploy cloud infrastructure including compute, storage and container platforms using infrastructure-as-code.
• Implement CI/CD pipelines for data jobs, APIs and ML models.
• Manage observability, performance and cost optimisation.

 
Data governance, privacy and compliance:

• Apply privacy methods such as k-anonymity, l-diversity, t-closeness and differential privacy.
• Manage PII minimisation, access controls, data lineage and audit readiness.
• Support approvals required under the client’s Data Sharing and Monetisation Policy.

 
Platform operations and support:

• Maintain platform stability and handle incidents, root-cause analysis and resolution.
• Monitor SLAs across pipeline freshness, model performance, API uptime and portal availability.
• Ensure business continuity and disaster recovery readiness.

 
Deliverables:

• Fully integrated data ingestion pipelines connecting downstream systems.
• Feature store and audience-builder pipelines with validated segmentation outputs.
• Machine learning models deployed to production with monitoring dashboards.
• Secure client-facing and internal insight portals.
• APIs for insight, scoring and ADR delivery.
• Cloud infrastructure deployed using best practices and IaC automation. Operational runbooks, documentation and handover materials.

 
Roles required update and optimized:
 
Leadership and delivery:
Role                             Level                Responsibility and Boundary
IT Delivery Manager     Senior               Owns end-to-end delivery and agile facilitation for the squad.
Programme-level coordination remains with the FTE Project Manager.
 
Product Owner                                     Provided by the FTE SM: Product Owner; no separate contractor Product Owner. Backlog and prioritisation owned there.
 
Architecture:
Role                             Level                Responsibility and Boundary
Solution                        Senior               Implements solution architecture under enterprise standards set
Implementation                                      by the FTE Data Architect / Solution Architect. No separate
Architect                                               enterprise-architecture role in the squad.
 
Data and AI Engineering:
Role:                            Responsibility and boundary                          
Big Data Engineer        High-volume ingestion, transformation and feature pipelines (e.g. Spark/Hadoop).
 
Cloud Engineer            Azure landing-zone, networking and core infrastructure. CI/CD and IaC now
(Azure/Databricks)        owned by the Platform Engineer.
 
Platform Engineer        Self-service golden paths, CI/CD and infrastructure-as-code across the Data Lake, GIS platform and Monetisation Portal.
 
GenAI / LLM                 Generative-AI / retrieval over data products; makes catalogue products
Engineer                      conversational and agent callable.
 
AI Agent / Agentic        Builds agents that accelerate the delivery squad and become productised Engineer                     features (e.g. analyst-agents over footfall and competitor data).
 
MLOps Engineer          Operationalises models — serve, monitor, retrain. Model build sites with the FTE ML Engineer
 
Application and experience:
Role                             Level                Responsibility and boundary
Full-Stack Developer    Senior               Front-end and back-end for portals, audience management and
score retrieval.
 
API Developer              Senior               Secure APIs exposing insights, scores, ADRs and ML outputs.
 
UX / Product                Senior               Designs the external client portal and insight-consumption
Designer                                              experience — currently unowned across the organisation.
 
Security, Privacy and Governance: