Back
High due to Tier-1 employer, generic title, mid-level experience, metro location, and broad data requirements.
Medium because core data engineering skills transfer broadly, but financial governance and domain experience increase specificity.
High due to explicit 5+ years, mandatory lakehouse/Spark/ETL skills and enterprise compliance expectations.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead complex, companywide technology initiatives and develop engineering best practices for large-scale technology solutions.
Design, code, test, debug, document, and review complex technology solutions aligned with tactical and strategic business objectives.
Lead projects and teams, mentor peers, and collaborate with cross-functional and industry experts to resolve complex technical issues and deliver measurable engineering outcomes during data center migrations and platform modernization efforts.
5+ years of Software Engineering experience or equivalent through work experience, training, military experience, or education.
Experience with Lakehouse engineering using open table formats like Iceberg, Delta, or Hudi and implementing Medallion architectures.
Proficiency in Python, SQL, Spark, and frameworks for data quality, observability, lineage, and SLAs.
Not explicitly mentioned: Notice period and explicit degree requirement.
Experienced in designing and optimizing metadata-driven ETL/ELT pipelines and distributed Spark workloads within lakehouse environments.
Skilled in building secure RESTful services and enterprise-grade production data pipelines with orchestration tools like Autosys or Airflow, ensuring SLA and alerting.
Background in financial data or risk systems, with strong understanding of data governance, infrastructure automation, CI/CD, cloud platforms, and potentially GenAI applications for metadata and pipeline optimization.