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Tier-1 brand, metro location, mid-level generalist title, and broad skillset increase applicant competition.
Core data engineering skills are transferable, but FinTech experience and AWS stack preferences increase domain bias.
Explicit 5+ years requirement, mandatory AWS big-data technologies, and leadership expectations raise strictness.
Develop and manage scalable, automated, fault-tolerant data pipelines using Spark, EMR, Python, Redshift, Glue, and S3 to support analytics, reporting, and operations.
Lead design and code reviews to enforce engineering best practices across data development, documentation, testing, and monitoring.
Drive data governance, cost optimization, measurement infrastructure for experiments, and provide self-service data access through metadata catalogs and governed query layers.
5+ years of data engineering experience.
Experience with data modeling, data warehousing, and building ETL pipelines using SQL and at least one scripting/programming language (Python, Java, Scala, or NodeJS).
Work Experience Required: Minimum 5 years in data engineering; Experience mentoring team members on best practices.
Not explicitly mentioned: formal degree requirements, notice period, or strict onsite/location mandates.
Experienced in leading and architecting large-scale data solutions, preferably with AWS data services (Redshift, S3, Glue, EMR, Kinesis, Lambda).
Strong familiarity with big data technologies (e.g., Hadoop, Hive, Spark, EMR) and building data platforms supporting batch and real-time analytics.
Capable of bridging technical and business requirements, providing technical leadership, mentoring engineers, and driving data governance and cost optimization strategies.