Python Data Engineer, SQL, Data Pipelines, FastAPI, Pytest, Git, 12+ Mths Cont NYC
JPC - J3594
Level 2: ( 5 to 8 yrs of industry Exp)
Loc: New York City, NY (3-days a week in office is mandatory, locals only)
Duration : 12 months
Interview Type : Final Round Inperson a MUST ( only local encouraged to apply )
GENERAL DESCRIPTION
We are seeking a highly motivated Python & Database Developer to join the Data Quality (DQ) Engineering team. The role focuses on building and enhancing enterprise-scale data quality solutions that enable data governance, regulatory compliance, operational excellence, and risk management across the organization.
The candidate will design and develop data quality frameworks, APIs, automation solutions, and database-driven applications that support onboarding, validation, monitoring, alerting, remediation, and reporting of data quality issues across multiple business domains. The role will contribute to the evolution of the DQ platform and related data management initiatives.
Years of experience:
5-7 years of Python & Database experience
Skills Required:
- Strong proficiency of Python with experience developing production-grade data processing pipelines
- Familiarity with object-oriented programming (OOP)
- Good knowledge of Database concepts and writing SQL queries and Stored Procedures and query optimization
- Expertise in FastAPI framework for building service endpoints and asynchronous processing systems
- Working knowledge of Unix
- Experience with version control tools (preferably Git)
- Writing unit tests (e.g. using pytest)
- Self-starter with ability to work in a fast paced environment and be able to work on multiple projects
- Experience of working in Agile Squads is good to have
- Finance data domain knowledge is also desired
Good to Have:
Understanding of model performance monitoring, model debugging, and logging systems within AI applications
Experience with containerization and deployment of ML services in enterprise environments
Experience with anomaly detection algorithms and techniques, particularly isolation forest, clustering, time series analysis, and pattern mining
Design and deploy generative AI solutions using LLMs and multimodal models to solve business problems
Expertise in pandas and familiarity in scikit-learn libraries for data manipulation and machine learning model implementation
Hybrid role:
3 days on-site / 2 days remote
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