Key Responsibilities:

1. Requirements Engineering & Stakeholder Management:

- Engage with commodity traders, risk managers, and business analysts to capture, analyze, and document requirements for market data and trading analytics platforms.

- Translate complex business logic into clear functional specifications, data mapping documents, technical user stories, and acceptance criteria for engineering teams.

- Act as the primary liaison between business end-users and cloud data engineers, ensuring aligned priorities and seamless delivery.

2. Data Analysis & Solution Architecture Support:

- Analyze raw commodity datasets (supply, demand, prices, inventory/stocks, cross-border imports/exports, trade flows) to identify patterns, data quality issues, and transformation requirements.

- Collaborate with data engineering teams on schema design, data modeling, and distributed pipeline architectures across cloud and on-premise platforms.

- Validate complex SQL transformations, business rules, and aggregation logic before deployment to downstream BI tools and analytics platforms.

3. Pipeline Monitoring, Data Governance & Quality:

- Partner with engineering teams to define and implement data quality frameworks, validation checks, and operational SLAs across batch workloads.

- Assist in troubleshooting data anomalies, source-to-target reconciliation, and pipeline outages impacting business-critical dashboards and reporting tools.

- Define and document data dictionaries, metadata, lineage, and data governance best practices for commodity market datasets.

Required Qualifications:

Experience & Education:

- Education: Bachelor's degree in Business Analytics, Computer Science, Information Systems, Finance, or a related quantitative field.

- Experience: 5+ years of experience as a Business Analyst, Data Analyst, or Systems Analyst working closely with production-grade ETL/ELT pipelines and scalable data processing environments.

Domain Expertise:

- Commodity Trading & Market Data: Strong domain expertise in commodity trading, including deep understanding of market fundamentals - commodity supply/demand, pricing benchmarks, physical/paper stocks, import/export trade flows, and vessel/shipping data.

Technical Skills & Tools:

- Database & Querying: Hands-on expertise in writing complex SQL queries for data profiling, source-to-target mapping, data validation, and transformation verification.

- Programming & Big Data Ecosystem: Functional understanding and hands-on exposure to Python, PySpark, Apache Spark, and Databricks for analyzing large-scale datasets.

- Orchestration & Data Integration: Familiarity with pipeline orchestration tools like Apache Airflow and cloud integration services like AWS Glue.

- Cloud & Storage Technologies: Familiarity with cloud storage and database systems including Amazon S3, Amazon Aurora, and object storage like MinIO.

- Analytics & Quality: Solid grasp of distributed data processing concepts, batch workload optimization, data quality controls, and pipeline troubleshooting.

Preferred Qualifications:

- Prior background working in energy, metals, agricultural commodities, or physical trading houses.

- Hands-on experience building or specifying datasets for BI and visualization tools (e.g., Power BI, Tableau, Looker).

- Familiarity with Agile/Scrum development practices and toolsets (Jira, Confluence).