Data Analyst

About MelliGold

MelliGold is a fintech startup operating at the intersection of the gold market and financial technology. We build intelligent and secure infrastructure for sourcing, trading, and managing gold transactions in the Iranian market.

Key Responsibilities

• Collect, clean, and analyze data across user behavior, transactions, pricing trends, and platform performance. 
• Build and maintain dashboards and reports to support decision-making for Product, Marketing, and Executive teams. 
• Identify patterns, market trends, and actionable insights to improve user experience and drive business growth. 
• Design and maintain data pipelines (ETL/ELT) and own the transformation layer — including dbt models and warehouse schemas — to ensure data quality and accuracy. 
• Collaborate with engineering teams to model, test, and govern data across the stack. 
• Analyze gold market volatility and its impact on user activity and pricing strategies. 
• Develop and deploy lightweight ML models to automate anomaly detection, forecasting, or user segmentation. 
• Evaluate and integrate AI/LLM tools where they reduce manual analysis overhead or enhance product intelligence. 
• Track and report on KPIs — conversion rate, CAC, CLV, and user retention — and translate findings for non-technical stakeholders.

Requirements

• Strong proficiency in SQL (complex queries, window functions, performance tuning) and spreadsheet tools (Excel, Google Sheets).
• Experience with BI and data visualization tools (e.g., Metabase, Tableau, Power BI, Looker). Solid understanding of statistical methods and A/B testing.
• Proficiency in Python for data wrangling, analysis, and automation (pandas, numpy, scikit-learn).
• Familiarity with modern data warehouse tools (Snowflake, BigQuery, or Redshift) and transformation frameworks (dbt). Strong analytical thinking, attention to detail, and communication skills.

Nice to Have

• Prior experience in financial markets, fintech, or cryptocurrency platforms. 
• Experience with ML frameworks (scikit-learn, XGBoost) or model deployment (MLflow, FastAPI). 
• Familiarity with pipeline orchestration (Airflow, Prefect) or streaming data tools (Kafka, Flink).
• Exposure to LLM APIs or building AI-assisted analytics tooling. Experience working in a fast-paced startup environment.