- Company Name
- Connor, Clark & Lunn Infrastructure
- Job Title
- Analyst, Data Analytics & Modelling
- Job Description
-
Job Title: Analyst, Data Analytics & Modelling
Role Summary:
Support asset management of renewable energy portfolio by designing, building, and maintaining automated data pipelines, financial models, and reporting workflows. Enable AI/ML integration and ensure data integrity across solar, wind, hydro, and other infrastructure assets.
Expactations:
- 1–3 years of experience in financial modelling (Excel, Power BI).
- 1–3 years of experience in data processing (pandas, openpyxl, pySpark, SQL).
- Familiarity with LLM tools (ChatGPT, GitHub Copilot, Claude).
- Strong problem‑solving mindset, attention to detail, and excellent communication.
Key Responsibilities:
- Design and maintain automated data ingestion pipelines for asset monitoring and reporting.
- Build and update financial and operating models in Microsoft Excel.
- Automate monthly reporting workflows through integration of external data sources (API, SFTP, databases).
- Develop validation layers with variance alerts and exception logging to ensure data quality.
- Create and manage site configuration registries, enabling new asset additions with minimal code changes.
- Contribute to AI enablement projects, including prototype development for LLM‑powered workflows.
- Document data lineage, produce runbooks, and facilitate knowledge transfer for operational continuity.
Required Skills:
- Quantitative degree (engineering, computer science, finance, mathematics).
- Proficiency in Excel, Power BI, and data processing libraries (pandas, pySpark).
- SQL and database integration experience.
- Ability to integrate data from heterogeneous sources (API, SFTP, external DBs).
- Experience with data platforms (Microsoft Fabric or equivalent) is an advantage.
- Hands‑on use of LLM and AI tools for practical tasks.
- Strong analytical, detail‑oriented mindset and effective communication with non‑technical stakeholders.
Required Education & Certifications:
- Bachelor’s degree in engineering, computer science, finance, mathematics, or related quantitative field.
- No specific certifications required, though knowledge of data governance or analytics certification (e.g., Microsoft Data Analyst Associate, Power BI certification) would be a plus.