Data Infrastructure & Engineering

Design and build scalable data pipelines processing financial transactions, market data, and user behaviour
Develop ETL processes integrating data from CDC, banking partners, fund administrators, and market data providers
Build real-time data systems for portfolio tracking, NAV calculations, and transaction processing
Maintain data quality frameworks ensuring accuracy of financial reporting and regulatory compliance
Optimize database performance for sub-second query response on millions of transactions

Analyze user behavior to identify drop-off points, conversion bottlenecks, and engagement patterns
Build dashboards and reporting systems for internal teams and regulatory submissions
Conduct cohort analysis to understand customer lifetime value, retention, and product usage
Develop attribution models to measure marketing channel effectiveness and CAC optimization
Create financial models for portfolio performance analysis and risk assessment

2-5 years experience in data science, data engineering, or analytics (fintech/finance experience strongly preferred)
Strong programming skills in Python (pandas, NumPy, scikit-learn, TensorFlow/PyTorch)
Proficiency in SQL and experience with relational databases (PostgreSQL, MySQL)
Experience building ETL pipelines and working with large datasets (millions of records)
Strong statistical knowledge and experience with A/B testing, regression analysis, time series
Ability to communicate complex technical concepts to non-technical stakeholders
Self-starter comfortable working in a fast-paced startup environment

Experience with cloud platforms (AWS, Azure, GCP) and big data tools (Spark, Airflow, Kafka)
Knowledge of financial products (mutual funds, pension schemes, market indices)
Familiarity with regulatory requirements in Pakistan's financial sector
Experience with visualization tools (Tableau, Power BI, Looker, or custom dashboards)
Background in behavioral economics or consumer psychology

See also