SUMMARY:
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 BoundaryIT 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 BoundarySolution 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 boundaryFull-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: Role Level Responsibility and boundaryCybersecurity Senior Secures platforms and pipelines handling subscriber and
Specialist geospatial data
Privacy Senior Implements privacy-enhancing techniques (k-anonymity, l-
Engineer diversity, t-closeness, differential privacy) in the pipelines
.(PETs) Compliance Senior Operational compliance evidence and audit readiness. Data
Analyst policy owned by FTE Data Privacy / Data Governance; model
risk owned by Responsible AI.
Quality, reliability and support: Role Level Responsibility and boundaryQA Engineer Senior Functional, data-accuracy and performance/load testing of data
(Data Products) products and visualisations
Infrastructure Senior Reliability engineering, observability and disaster-recovery
Engineer (SRE) readiness.
POSITION INFO:
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:
