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Tier-1 brand, popular Data Engineer title, mid-level experience range increase applicant competition.
Core data engineering skills are broadly transferable across industries despite fintech context.
Explicit 5+ years requirement and multiple mandatory technical skills create strict screening.
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Design and deliver scalable, high-quality data pipelines and modern data warehouse solutions using technologies like Spark, Python, and Hive.
Provide technical leadership and mentorship to data engineering teams, establishing best practices and driving innovation in data architecture and pipeline management.
Build and optimize data modeling frameworks leveraging modern architectural patterns (e.g., Medallion Architecture) to support reliable, efficient global data solutions.
Minimum 5 years of relevant work experience with a Bachelor's degree, or 2 years with an advanced degree; relevant experience varies by education level.
Proficiency in Hadoop ecosystem technologies (HDFS, Hive, Spark, EMR), Python, SQL, and data pipeline orchestration tools like Apache Airflow or Oozie.
Experience in designing large-scale data architectures, data quality frameworks, and production-grade performance optimization in data pipelines.
Hands-on experience with cloud data platforms (AWS or Azure) and version control systems (Git); familiarity with CI/CD pipelines and data visualization tools (Tableau or Power BI).
Experienced in leading global, large-scale data engineering projects with focus on scalability, data quality, and multi-region delivery.
Strong technical leader capable of mentoring teams and collaborating cross-functionally with business and technical stakeholders.
Proficient in modern data architectures and frameworks, utilizing emerging technologies like Generative AI to enhance data engineering workflows.