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Tier-1 employer, mid-level 2–5yr role in metro with broad data skill requirements increases applicant competition.
Core data engineering skills transfer across industries, but financial/regulatory domain knowledge adds moderate bias.
Explicit 2–5 year requirement plus mandatory data platform, cloud, and Airflow skills makes filtering strict.
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Develop, maintain, and optimize production-grade data pipelines supporting analytics, credit modeling, and financial reporting.
Manage data onboarding, processing, and delivery for scaling credit data engineering across new regions, ensuring reliability, compliance, and timely execution.
Collaborate with cross-functional teams to translate business and data requirements into scalable technical solutions using Python, SQL, Airflow, and cloud-native platforms.
2–5 years of experience in software engineering with a focus on data engineering or data platforms.
Bachelor's or Master's degree in Computer Science, Information Systems, Engineering, or related field.
Strong proficiency in Python and SQL; hands-on experience building and maintaining production-grade data pipelines.
Experience working in a cloud-native environment (GCP, AWS, or Azure); familiarity with workflow orchestration tools like Apache Airflow and BigQuery or similar data warehousing technology.
Experienced in designing scalable and reliable data pipelines for regulated financial services or similar complex domains.
Practiced in software engineering best practices including version control (Git), code reviews, testing, CI/CD, and performance optimization.
Able to collaborate across diverse teams to balance technical quality with delivery timelines in a large-scale cloud-native environment.