Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Tier-1 brand, mid-level data engineering role, and broad popular cloud-data skillset increase competition.
Platform-specific cloud data skills transfer across industries, but enterprise/regulatory context increases domain specificity.
Mandatory 6+ years and specific Snowflake, Azure/Databricks, PySpark, and cloud platform expertise enforce strict filters.
Lead end-to-end delivery of complex finance-related data engineering projects, balancing speed, quality, and measurable business outcomes.
Architect, build, and maintain scalable cloud data pipelines and platforms using technologies like Azure Data Factory, Snowflake, Databricks, Spark/PySpark, SQL, and Python.
Collaborate cross-functionally with finance partners, analytics, architecture, and platform teams to enable reliable data solutions for analytics, reporting, and AI/ML use cases.
Bachelor's degree in computer science, information systems, data engineering, analytics, cloud engineering, or related field.
6+ years of experience in data engineering or related roles with demonstrated technical leadership or mentoring experience.
Hands-on expertise with Snowflake, Azure Data Factory, Azure Data Lake, Databricks, Spark/PySpark, SQL, and Python in cloud-based data platforms.
Experience with cloud infrastructure concepts relevant to data engineering including storage, compute, networking, security, CI/CD, infrastructure-as-code, and data governance.
Experienced technical leader with proven ability to deliver large-scale, cloud-native finance data platforms and pipelines.
Strong collaborator skilled in partnering with diverse teams including finance business partners, analytics, architecture, and platform engineering.
Deep understanding of modern data engineering practices, cloud infrastructure, data governance, security, and performance optimization in a fast-paced environment.