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Tier-1 brand, mid-level generalist title, and metro location increase applicant competition significantly.
Core data engineering skills are transferable, but credit/risk and regulated-finance experience increase domain specificity.
Mandatory coding/data pipeline skills, cloud and data platform experience, and regulated-finance context enforce strict screening.
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Develop and maintain production-grade data pipelines using Python and SQL to support analytics, credit modeling, and financial reporting.
Design and operate workflow orchestration solutions (e.g., Apache Airflow) for scheduled and event-driven data processing at scale.
Collaborate with cross-functional teams to translate business and data requirements into compliant, reliable technical data solutions, supporting new region expansion efforts.
2–5 years of software engineering experience, preferably with data engineering or data platforms.
Bachelor's or Master's degree in Computer Science, Information Systems, Engineering, or a related field.
Strong proficiency in Python and SQL with hands-on experience building production-grade data pipelines.
Experience in cloud-native environments (GCP, AWS, or Azure) and familiarity with data warehousing technologies such as BigQuery.
Experienced in building scalable data engineering solutions in regulated financial services environments supporting credit, risk, or financial reporting domains.
Practices software engineering best practices including version control, code reviews, testing, CI/CD, and data quality frameworks.
Comfortable working with workflow orchestration tools like Apache Airflow and migrating legacy schedulers, with ability to optimize pipeline performance and maintain SLA adherence.