The role of the Tech Lead focuses on analytics, systems, and project delivery, specifically related to the extraction, transformation, and creation of analytically ready data for various types of analytics in a financial services environment. This role requires a blend of strong technical skills, domain knowledge in credit risk and banking products, and the ability to collaborate with cross-functional teams. The primary responsibilities include managing and optimizing data processes, ensuring data quality, and developing data definitions and routines that support descriptive, diagnostic, predictive, and prescriptive analytics.
Qualifications:
- Experience: 6+ years in technology and data management, particularly within credit risk and banking products.
- Technical Skills:
- Extensive commercial experience with SAS (data manipulation and cleansing).
- Proficiency in SQL (commercial).
- Python programming skills.
- Knowledge of working with multiple data formats like XML, CSV, flat files, SAS datasets, and Teradata database/server.
- Domain Knowledge: Strong understanding of credit risk, banking products, and financial services data.
- Project Delivery: Proven track record of delivering business and technology solutions, especially in engineering complex data processes.
- Communication: Excellent written and verbal communication skills to interact effectively with various stakeholders.
- Collaboration: Ability to work well in cross-functional teams, collaborating with departments like Risk Analytics, Product Management, and Collections.
Key Responsibilities:
- Collaboration: Work closely with business units to understand credit risk-related data, identifying data elements, reference data, metadata, and data lineage.
- Data Analysis: Analyze source system data, identify derived measures, and develop data definitions.
- Data Extraction & Transformation: Develop and optimize data routines for extraction, transformation, and preparation of data for analytical use.
- Data Quality & Remediation: Perform data quality analysis and resolve issues, ensuring clean and usable data for analytics.
- Process Engineering: Engineer and optimize data processes within the Analytics Data Layer, ensuring efficient workflows.
- Root Cause Investigation: Investigate data issues to identify root causes and develop necessary business rules.
This role is ideal for a seasoned professional with expertise in credit risk and banking products, who is passionate about data analytics and enjoys solving complex data challenges. With a combination of technical skills and business acumen, this individual will have the opportunity to lead innovative projects that directly contribute to the success of analytics initiatives within the financial services sector.