Job Description: Data & Integration Engineer

Company: Peak Energy

Department: Technology / Data & Analytics

Experience: 4–7 Years

Employment Type: Full-Time

Location: As per hiring requirements

Reporting To: To be confirmed

1. About Peak Energy

Peak Energy is a clean energy platform that develops, finances, builds, and operates utility-scale solar, rooftop generation, onshore wind, and battery storage solutions across Asia-Pacific.

With more than 300 MW of operating assets and 1 GW of projects in development across multiple Asian markets, Peak Energy is building a dynamic renewable energy platform. Wholly owned by Stonepeak, a leading infrastructure and real assets investment firm, Peak combines financial strength, technical expertise, and local market knowledge to deliver high-quality clean energy solutions.

At Peak Energy, we encourage innovation, collaboration, and continuous improvement as we accelerate the transition to renewable energy.

Join us and help build a sustainable future through technology, data, and innovation.

2. Role Overview

Peak Energy is seeking a skilled Data & Integration Engineer to design, develop, and maintain reliable data pipelines and integration solutions connecting core enterprise applications.

The role will focus on building a centralized, governed data platform to support business analytics, reporting, automation, and approved AI use cases. The successful candidate will combine hands-on data engineering expertise with data quality management, system integration, testing, monitoring, and production support.

The candidate will collaborate with business stakeholders, technology teams, application owners, and external implementation partners to deliver secure, scalable, and maintainable data solutions.

3. Key Responsibilities

A. Microsoft Fabric & Data Engineering

• Develop and maintain data pipelines using Microsoft Fabric Data Factory, notebooks, SQL, Python, and Spark.
• Implement Bronze, Silver, and Gold data layers using OneLake and Lakehouse architecture.
• Build batch-processing and API-based data ingestion solutions from enterprise applications.
• Optimize pipelines for performance, scalability, reliability, and maintainability.
• Manage pipeline scheduling, deployment, monitoring, and production troubleshooting.
• Implement error handling, recovery mechanisms, and operational controls.

B. Data Integration & Quality

• Design data mappings, transformations, identifiers, and validation rules across enterprise systems.
• Develop and maintain REST API integrations and approved file-based interfaces.
• Integrate data from finance, project management, commercial, and operational applications.
• Implement automated data-quality checks, reconciliation controls, and exception reporting.
• Investigate data inconsistencies and coordinate corrective actions with application owners and business stakeholders.
• Conduct integration testing to ensure data accuracy, completeness, and consistency.

C. Analytics & Data Governance

• Develop curated datasets and dimensional models for Power BI, operational reporting, and business analytics.
• Prepare structured datasets for dashboards, semantic models, reconciliation, and approved AI applications.
• Maintain technical metadata, data definitions, lineage, transformation logic, and documentation.
• Support Microsoft Purview cataloguing and lineage activities, where applicable.
• Apply approved data access controls, privacy requirements, security standards, and retention policies.
• Collaborate with business teams to translate reporting requirements into reliable data products.

D. Engineering Practices & Production Support

• Apply Git-based version control, peer reviews, automated testing, CI/CD, and release-management practices.
• Implement logging, monitoring, alerting, and observability for data pipelines and integration services.
• Troubleshoot production incidents, identify root causes, and implement corrective actions.
• Maintain technical documentation, runbooks, data mappings, test evidence, and operating procedures.
• Coordinate with technology vendors and implementation partners to ensure solution quality and knowledge transfer.
• Identify opportunities to automate manual processes, improve operational efficiency, and reduce recurring support issues.

4. Required Qualifications & Experience

• Bachelor's degree in Computer Science, Data Engineering, Information Technology, Information Systems, Engineering, or a related discipline.
• Approximately 4–7 years of relevant experience in data engineering, analytics engineering, enterprise integration, or data-platform delivery.
• Hands-on experience with Microsoft Fabric, including Data Factory, Lakehouse, OneLake, and notebooks.
• Strong working knowledge of SQL and Python.
• Practical experience with Apache Spark or equivalent distributed data-processing technologies.
• Experience designing and maintaining ETL/ELT pipelines and REST API integrations.
• Understanding of medallion architecture, dimensional modelling, and analytical data preparation.
• Experience implementing data-quality checks, reconciliation controls, and exception handling.
• Familiarity with Git, CI/CD, automated testing, deployment, monitoring, and production support.
• Understanding of data governance, metadata management, lineage, access controls, and information security.

5. Preferred Qualifications

• Microsoft certifications in Fabric, Azure data services, Power BI, or related technologies.
• Experience with Microsoft Purview and Power BI semantic models.
• Exposure to Microsoft Dynamics 365 Business Central, Salesforce, or other enterprise applications.
• Experience integrating finance, project management, commercial, or operational data.
• Familiarity with cloud-based data platforms and secure integration patterns.
• Experience working with external implementation partners and managing technical deliverables.
• Exposure to AI-enabled analytics, automation, and governed data preparation for AI applications.

6. Key Competencies

• Technical Expertise: Strong data engineering and integration capabilities.
• Analytical Thinking: Ability to investigate complex data issues and identify root causes.
• Data Quality: Commitment to data accuracy, completeness, and consistency.
• Problem-Solving: Ability to troubleshoot technical issues and implement effective solutions.
• Collaboration: Effective communication with technical teams and business stakeholders.
• Ownership: Accountability for data solutions, production reliability, and documentation.
• Continuous Improvement: Ability to identify automation and process improvement opportunities.

7. Key Performance Indicators (KPIs)

Performance may be evaluated against agreed targets for:

• Reliability and successful execution of scheduled data pipelines.
• Data accuracy, completeness, and reconciliation.
• Integration reliability across enterprise applications.
• Timely resolution of pipeline failures and data-quality issues.
• Pipeline performance and processing efficiency.
• Delivery of agreed technical milestones.
• Compliance with data governance and documentation standards.
• Reduction in manual data processing and recurring operational issues.

Specific KPI targets will be established by the hiring manager based on business and operational requirements.

8. Key Stakeholders

The role will collaborate with Technology and Data & Analytics teams, Finance, Project Development, Commercial, Operations, Information Security, Data Governance, application owners, and external technology partners.

9. Expected Outcomes

The successful candidate will help establish a reliable and governed data platform that improves data quality, connects enterprise applications, enables trusted business reporting, reduces manual effort, and supports scalable analytics and automation.