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Tier-1 brand, mid-level data engineer in metro with broad Big Data/PySpark requirements increases applicant competition.
Requires Financial Services experience and Big Data domain knowledge, limiting cross-industry transferability.
Explicit 5–7 years plus mandatory Big Data, PySpark, and financial domain experience makes shortlisting strict.
Design, build, and optimize scalable data pipelines and architectures using Big Data technologies and scripting languages like Python and SQL.
Collaborate with business analysts and technology teams to understand data requirements, recommend data integration solutions, and support high-performance data platforms.
Ensure data quality, security, and compliance through rigorous testing, monitoring, and performance optimization of data workflows.
5 - 7 years of relevant experience in Financial Services industry in an Applications Development role.
Proficiency in Big Data technologies such as Cloudera, Hive, Python, Java/PySpark and data analysis/modeling skills.
Bachelor’s degree or equivalent experience required.
Knowledge of ETL concepts; experience with relational databases like Oracle or SQL Server is a plus.
Experienced in independently managing data engineering deliverables with minimal supervision within Financial Services.
Strong technical expertise in distributed systems, data pipeline architecture, and cloud data warehouses for solving complex data infrastructure challenges.
Capable of balancing technical performance optimization with risk, privacy, and compliance requirements (e.g., GDPR).