Job Level
Senior
Tech Stack
AirflowAWSAzureCloudETLGoogle Cloud PlatformHadoopPythonPyTorchScikit-LearnSparkSQLTableauTensorflow
About the role
- Perform data-driven risk assessments and compliance monitoring; analyze complex datasets to identify patterns, anomalies, and emerging risks impacting regulatory and internal compliance.
- Design, develop, and maintain automated financial reports and dashboards, applying AI-driven methods to streamline data processing and reporting.
- Leverage LLMs and AI tools for intelligent data validation, anomaly detection, reconciliation, and report generation.
- Collaborate with finance, payment operations, and engineering teams to gather requirements and implement AI-enabled reporting solutions.
- Enhance ETL processes with automation and machine learning techniques to improve financial data quality and reduce manual intervention.
- Develop reusable AI/ML components for financial reporting tasks such as fraud detection signals, transaction pattern analysis, and reconciliation workflows.
- Integrate financial reporting systems with generative AI tools (e.g., ChatGPT, LangChain) for query handling, report explanation, and natural language summaries.
- Partner with data engineers to ensure robust financial data models, pipelines, and compliance with internal and regulatory standards.
- Perform advanced troubleshooting, leveraging AI-assisted diagnostics to identify and resolve discrepancies in settlement and merchant data pipelines.
- Stay up to date with AI and big data technologies, evaluating opportunities to bring innovation into financial reporting workflows.
Requirements
- Bachelor's or Master's degree in Computer Science, Data Engineering, Finance, or a related field.
- 4+ years of experience in data engineering or reporting, with exposure to financial reporting and transaction data preferred.
- Strong skills in SQL and Python.
- Experience in ETL orchestration tools (Airflow, dbt, Talend, or similar).
- Hands-on experience with AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn).
- Familiarity with LLMs and generative AI tools.
- Experience integrating AI into financial or reporting workflows (e.g., anomaly detection, NLP-driven reporting).
- Proficiency with data visualization tools (Tableau, Power BI, QuickSight).
- Familiarity with cloud platforms (AWS, GCP, Azure) and big data technologies (Spark, Presto, or Hadoop).
- Solid understanding of financial transaction data, reconciliation processes, and settlement workflows.
- Strong problem-solving, documentation, and communication skills.
- Nice to have: experience applying prompt engineering and embeddings for financial data analysis.
- Nice to have: Knowledge of MLOps practices for deploying and monitoring AI-driven reporting pipelines.
- Nice to have: Exposure to fintech, payments, or e-commerce reporting systems.