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Tier-1 brand, mid-level generalist title, metro location, and broad technical requirements drive high applicant competition.
Core data engineering and ML skills are transferable, but finance domain experience moderately increases sensitivity.
Explicit 5+ years requirement plus mandatory data, PySpark, Spark, Kafka, and RAG skills enforce high shortlisting strictness.
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Lead complex, large-scale technology initiatives and establish companywide engineering standards for scalable, modernized applications.
Design, develop, and review complex data engineering solutions involving Python, PySpark, and large-scale data pipelines with platforms like Apache Spark and Lakehouse technologies.
Lead technical teams and mentor peers while driving decision-making and engineering discipline for data engineering projects including agentic AI and retrieval-augmented generation architectures.
5+ years of Software Engineering experience (work experience or equivalent demonstrated through experience, education, or training).
Mandatory coding skills in Python and PySpark with experience in RDBMS technologies (preferably SQL Server).
Experience designing and optimizing large-scale data pipelines, including familiarity with streaming technologies like Kafka.
Experience working in Agile/Scrum environments.
Experienced data engineer capable of leading companywide modernization efforts and setting engineering standards for complex distributed systems.
Proficient in building and optimizing scalable data architectures incorporating stream processing and Lakehouse data layers, and deploying retrieval-augmented generation AI solutions.
Strong leadership presence with demonstrated ability to mentor technical teams, influence multiple stakeholders, and communicate complex technical concepts effectively across audiences.