Data Architect

We are looking for a Data Architect to serve as the primary architect of our data ecosystem. This is not a traditional “modeling-first” role; instead, you will be responsible for building the structural integrity, governance, and pipeline architecture required to transform raw commodity and trading data into a reliable strategic asset. You will lay the groundwork that enables all future analytics, risk reporting, and automation initiatives.

Key Responsibilities:

• Data Architecture & Strategy: Design and implement a robust data infrastructure tailored for energy trading, ensuring high availability and low latency for both market and internal data
• Pipeline Development: Build and maintain scalable ETL/ELT pipelines to ingest data from ETRM systems, external market feeds (e.g., price assessments), and financial derivative platforms
• Data Governance & Security: Establish frameworks for data quality, lineage, and master data management. Ensure all data handling aligns with industry-specific secure usage guidelines and regulatory requirements
• Foundational Modeling: Focus on structural data modeling (e.g., Star Schema, Data Vault) rather than predictive modeling to create a “single source of truth” for the department
• Tooling & Integration: Evaluate and deploy the core data stack—selecting the right warehouses, orchestration tools, and integration layers to support Business Analysts and functional teams

Required Qualifications & Skills:

• Experience: 5+ years in Data Engineering or Data Architecture, ideally within a high-stakes environment like commodity trading or fintech
• Expert-level SQL and proficiency in Python for data engineering tasks
• Hands-on experience with modern data warehouse platforms (e.g., Snowflake, BigQuery, or Databricks)
• Experience with orchestration tools (e.g., Airflow, dbt) and API integrations
• Domain Expertise: Understanding the nuances of energy market data—handling time-series data, trade lifecycle states, and complex financial instruments will be added advantage
• Execution: Proven track record of taking a “messy” data environment and organizing it into a functional, governed ecosystem

Core Competencies:

• Structural Thinking: The ability to see the “big picture” of how data flows across Infrastructure, Cybersecurity, and App Dev teams
• Attention to Detail: A rigorous focus on data accuracy and validation—recognizing that in trading, a decimal error can have significant financial consequences
• Consultative Approach: Ability to work with internal stakeholders to define requirements and translate business needs into technical schemas