Back
Tier-1 brand, metro location, and common mid-level Spark/Python data skillset increase competition.
Core data engineering skills transfer across industries, but finance-specific security and tooling increase domain bias.
Mandatory 3+ years plus Spark/Python, Big Data AWS stack, scheduling and security requirements create strict filters.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, develop, test, and maintain critical data pipelines and architectures supporting business functions within US Wealth Management.
Utilize Spark-based frameworks and Python for end-to-end ETL/ELT and reporting solutions, ensuring data security and high performance including multi-threading and batch processing.
Leverage authorized AI-assisted development tools to improve code quality and delivery speed, validate AI outputs, and contribute reusable patterns to the team.
3+ years of applied experience with formal training or certification in software engineering concepts.
Hands-on experience with Big Data stack: Spark, Python (Pandas, Spark SQL), and cloud AWS data services (Lake Formation, Glue ETL/EMR, S3, Glue Catalog, Athena, Kinesis/MSK, Airflow/Lambda + Step Functions + Event Bridge).
Strong knowledge of RDBMS, NoSQL databases, Linux/UNIX; performance tuning in Python and Spark; use of Autosys or Control-M scheduler.
Experience using enterprise-authorized AI-assisted software development tools with ability to validate AI-generated outputs for correctness, performance, and security.
Experienced in end-to-end data lifecycle management and big data technologies within cloud environments, particularly AWS data services.
Demonstrates strong programming skills in Python and Spark, with good understanding of database types and security concepts like IAM, encryption, and KMS.
Comfortable integrating AI-assisted coding tools responsibly within SDLC, with focus on secure, reliable, and high-quality software delivery in agile frameworks.