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Skills
Application Programming Interface (API)unmatched
Cisco Unityunmatched
Cloud Computingunmatched
Data Managementunmatched
Data Processingunmatched
Data Qualityunmatched
Database Extract Transform and Load (ETL)unmatched
Dental Insuranceunmatched
Engineeringunmatched
Financial Analysisunmatched
Financial Managementunmatched
Health Insuranceunmatched
JSONunmatched
Microsoft SQL Serverunmatched
Microsoft Windows Azureunmatched
Python Programming/Scripting Languageunmatched
Quality Controlunmatched
SQL (Structured Query Language)unmatched
Scalable System Developmentunmatched
Structured Dataunmatched
Systems Administration/Managementunmatched
Unstructured Dataunmatched
Vision Planunmatched
XML (EXtensible Markup Language)unmatched
Description
Benefits:
401(k)
Dental insurance
Health insurance
Paid time off
Vision insurance
Seeking a Data Engineer responsible for designing, developing, and maintaining scalable enterprise data pipelines supporting advanced fraud analytics. The candidate will manage ingestion, transformation, quality control, and optimization of structured and unstructured data across cloud-based analytics platforms.
Responsibilities
Should bring a minimum of three (3) years of professional experience in data engineering or a related field.
Demonstrate the ability to design, build, and maintain scalable ETL pipelines across diverse data sources.
Should apply strong SQL and Python skills, or equivalent technologies, to ingest and transform data from flat files, JSON, XML, Excel, APIs, graph databases, with flexibility to adapt to additional formats and sources as needed.
Should possess experience loading, managing, and optimizing data within platforms such as Databricks Unity Catalog and SQL Server managed instances, including work with streaming and batch ingestion frameworks and modern Lakehouse architecture.
Should exhibit strong capabilities in implementing standard quality control processes to ensure data quality, lineage, reliability, and performance while collaborating effectively with cross‑functional teams.
Must have familiarity with data governance, data quality, and data management practices consistent with enterprise data management (EDM) standards.
Must have experience supporting fraud detection, anomaly detection, or financial oversight analytics environment preferred.