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Tier-1 fintech brand, mid-level generalist role, metro location, and broad data skillset increase competition.
Core data-engineering skills are transferable, though financial/regulatory experience is preferred.
Explicit years requirement and mandatory data-engineering tech stack increase filtering, but some flexibility exists.
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Develop and maintain scalable, reliable production-grade data pipelines using Python, SQL, and cloud-native technologies to support credit analytics, modeling, and financial reporting.
Onboard and process data from internal and external sources including new regional datasets, ensuring compliant and timely data delivery.
Collaborate with cross-functional teams to translate business needs into technical data solutions while ensuring data quality, pipeline performance, and SLA adherence.
2–5 years of software engineering experience with strong exposure to data engineering or data platforms.
Proficiency in Python and SQL with hands-on experience building and maintaining production data pipelines.
Experience with cloud-native environments (GCP, AWS, or Azure), workflow orchestration tools like Apache Airflow, and data warehousing technologies such as BigQuery.
Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or related field.
Experienced in developing scalable data solutions supporting credit, risk, or financial reporting domains, particularly in regulated financial services environments.
Demonstrated ability to ensure pipeline reliability, optimize performance, and handle production debugging in cloud data platforms.
Comfortable working in cross-functional teams translating complex business requirements into technical implementations adhering to software engineering best practices and compliance standards.