Data Integration Developer SQL, Python & Azure Data Factory

Axiom Path•Jersey City, NJ

87d•Hybrid

About The Position

Join the technology organization of a global financial institution supporting critical compliance, analytics, and reporting initiatives. This collaborative team brings together data developers, business analysts, and data analysts to build dependable data solutions for a highly regulated environment. The group values technical quality, careful validation, strong problem-solving, and close partnership between development and quality assurance.

Requirements

3-5 years of professional experience in data development, ETL engineering, data integration, or a closely related role.
Strong hands-on SQL skills, including complex queries, joins, transformations, troubleshooting, and performance tuning.
Experience developing scripts or data-processing workflows with Python.
Practical experience with Azure Data Factory or another enterprise ETL platform.
Knowledge of data warehousing concepts, dimensional modeling, and structured data delivery.
Demonstrated ability to test and validate data pipelines, transformation logic, and downstream outputs.
Experience investigating data discrepancies and performing source-to-target reconciliation.
Strong development discipline with an understanding of quality assurance and defect-management practices.
Ability to collaborate effectively with business analysts, data analysts, developers, and other technical stakeholders.
Detail-oriented work style with strong analytical, documentation, and problem-solving skills.

Nice To Haves

Financial services, banking, compliance technology, or regulatory data experience is preferred.

Responsibilities

Develop scripts, processes, and tools that ingest, transform, validate, and deliver data for analytics and reporting.
Write efficient SQL queries, stored procedures, and data transformation logic.
Design and maintain ETL pipelines using Python, Azure Data Factory, or comparable data integration tools.
Automate recurring data workflows to improve reliability and reduce manual processing.
Perform development-level quality assurance, including data reconciliation, defect identification, and regression testing.
Monitor and improve data quality, completeness, consistency, and accuracy.
Troubleshoot data pipeline issues and optimize SQL queries and ETL processes for performance.
Partner with business analysts, data analysts, and developers to translate reporting requirements into reliable technical solutions.
Document data workflows, validation rules, dependencies, and technical processes.