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Data Analyst with 1+ years in SQL & Python for FinTech & Financial Services.
Detail-oriented Data Analyst with 2+ years of experience analysing large-scale datasets, building BI dashboards, and supporting strategic decision-making in fintech and financial services environments. Proficient in advanced SQL (complex joins, window functions, CTEs, query optimisation) on ClickHouse, Starburst/Hive, and PostgreSQL; skilled in Python (pandas, NumPy, scikit-learn) for EDA, regression, classification, and predictive modelling. Experienced in designing and maintaining ETL/ELT pipelines using Apache Airflow, developing reporting frameworks, and applying diagnostic and statistical analytics to translate complex data into actionable insights. Familiar with Generative AI and LLM-based tools for AI-assisted analytical workflows. Strong foundation in A/B testing, data governance, and cross-functional agile collaboration.
Cultural Fit Analysis
The candidate's experience across fintech and financial services, coupled with a diverse set of technical skills, suggests adaptability to different project types and business domains. Their involvement in cross-functional collaboration and data governance aligns with a culture that values teamwork, data integrity, and responsible data usage. However, the lack of diverse project types outside of financial data analysis might indicate a narrower scope of experience for broader cultural fit in highly varied data environments.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical thinking, problem-solving skills, and attention to detail, as evidenced by their work in identifying drop-off points, building risk models, and ensuring data accuracy. Their experience in cross-functional collaboration with engineering teams and communication of findings to various stakeholders indicates good teamwork and communication skills. The mention of agile environments suggests adaptability and operational fit for fast-paced teams.