Back
Metro location, popular Data Engineer title, and generalist skillset increase competition despite weaker employer brand.
Core data engineering skills transfer across industries, though investment/finance preference moderately increases domain specificity.
Explicit 7-10 years plus mandatory Airflow, Spark, Python, and SQL raises screening strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, build, and maintain data pipelines to ingest and organize datasets for the investment and analyst teams.
Automate ingestion, transformation, and data-quality workflows to enhance speed and reliability of data onboarding.
Support AI roadmap by processing documents for RAG, managing vector databases, and enabling integrated dataset querying for insights and reporting.
4-5+ years of hands-on data engineering experience, preferably in financial services or data-intensive environments.
Strong experience with Apache Airflow for pipeline orchestration and Apache Spark for large-scale data processing.
Proficient in Python and SQL with focus on clean, maintainable production-quality code.
Work Experience Required: 7-10 years as per job snapshot.
Experienced operating in loosely defined and evolving scopes with ability to self-organize and collaborate with stakeholders to prioritize and deliver.
Background in designing data models and organizing datasets for integration and efficient querying.
Familiarity with financial services or investment management data environments and emerging AI/LLM technologies (RAG, vector DBs) is advantageous.