Associate Data & ML Engineer

Key Responsibilities

1.  Design, build and maintain end-to-end automated data pipelines from internal and external sources into a central data platform including scheduling, monitoring and resolving issues for assigned workflows: 
   1. Platform maintenance and development of Globeleq’s Central Asset Management system (CAMs), including calculation development, on-boarding assets and equipment, and a continuous improvement focus. 
   2. Develop, implement and extend the integrated SSOT database, consolidating data from CAMs, ERP, OT/IoT, SharePoint and other platforms according to agreed standards ensuring scalable data flows. 
   3. Develop modular and scalable data ingestion using API integrations, SQL stored procedures and ETL frameworks, maintaining reliable, automated data ingestion between internal and external platforms to the SSOT database. 
   4. Support and provide data development solutions. Be a practical driver and enforcer of the Data Transformation plan on behalf of the Data Engineering Manager; escalating risks and developing solutions. 
2. Design and develop scalable data models (staging, core, marts, feature sets) that support strategic reporting, advanced analytics and ML. 
   1. Develop scalable data models across clearly defined business layers, including staging (raw landed data), core (cleaned and standardised single source of truth), marts (business-ready views for specific domains), and feature sets (model-ready tables for machine learning and advanced analytics). 
   2. Develop and support workflow automation and lightweight data applications using tools such as Power Apps and Power Automate, integrating these solutions with the core data platform to enable efficient business processes. 
   3. Develop ML algorithms, dedicated toward anomaly detection, prediction and neural networks. Ensuring all data models, pipelines and storage approaches are AI/ML-ready, including feature-ready datasets for pattern recognition, prediction and anomaly detection 
   4. Own end-to-end development and processing (data algorithms and ML solutions) 
3. Technical platform development, data orchestration and data management 
   1. Adhere to governing data management, security and governance standards in line with the Data Governance Policy (Data Engineering, Audit and Risk, Cyber Security and IT requirements). 
   2. Maintain, document and track comprehensive technical documentation and change management records for architectures, pipelines, automations, environments and access. 
   3. Work with divisional data owners to reduce data silos, standardise data flows and ensure adherence to agreed standards and timelines. 

Skills and Competencies

1. Full-stack data engineering competence: 
   1. API integration (REST/JSON, auth, pagination, error handling) 
   2. ETL/ELT orchestration and job scheduling (Automated workflows) 
   3. Data modelling (staging, core, marts, feature sets); production operations (monitoring, alerting, incident response) 
   4. Strong SQL; proficiency in Python for data engineering and ML-enabling tasks; and solid programming foundations in Python, SQL and/or C#
   5. Ability to make scalable architectural decisions and prepare data for ML and model integration into workflows.
   6. Solid ML foundations (feature engineering, evaluation, overfitting, drift) and ability to design data pipelines that are fit for ML. 
   7. Automated workflow development (Power Automate and PowerApps) 
2. Strong, hands-on engineering mindset; comfortable taking technical ownership of assigned work: 
   1. Proven ability to design and lead data platform or data product builds. 
   2. Identifies problems, proposes solutions and drives implementation. 
   3. Clear, structured communication skills; and can explain technical aspects to non-technical stakeholders and leadership. 
3. Strong systems thinking and architecture skills: designs for scalability, maintainability and AI/ML-readiness from the outset.
   1. Strong engineering discipline: version control, testing, deployment processes, documentation and incident handling. 
   2. Enjoys building automation, integrations and ML-ready datasets. 
   3. Cross-team coordination. Strong ability to work with multiple divisions, follow up with stakeholders and enforce agreed standards and timelines.