Data Engineering Analyst en World Business Lenders, LLC

About World Business Lenders Learn more about us: [Who We Are] World Business Lenders (WBL) is proud to offer short-term, real estate-backed commercial loans to a diverse range of small and medium-sized businesses across the United States, especially those who may find it challenging to secure traditional financing. Typically, the work schedule is from 9:00am to 6:00pm Eastern Time, Monday through Friday, though there might be times when additional hours are needed to meet business demands. We’re looking for someone with strong communication skills in both English, both spoken and written. Please note that all resumes should be submitted in English. We are seeking two Data Analysts, one specializing in data engineering & migration, and another focused on data pipeline & automation. Candidates with strengths in either area are encouraged to apply. Key Responsibilities: Improved data pipelines (via automation) with quality checks to ensure data accuracy, consistency, and reliability throughout the entire data processing workflow, reducing manual intervention and minimizing errors. Migration of legacy systems to new systems, involving thorough analysis, seamless data transfer, and integration to enhance system performance and maintain continuity without disrupting existing operations. Requirements Required Education: A Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field is preferred — but we also highly value equivalent hands-on experience! Required Experience: 4 to 7 years of experience as a Data Engineer. We value practical, hands-on expertise that goes beyond just theoretical knowledge. Required Background / Industry Experience: Required for both roles: Experience working with cloud-based data platforms and environments Strong SQL skills with data modeling experience for data warehouses Strong Python skills (especially notebooks) for building and maintaining data workflows Experience developing and maintaining ETL/ELT processes Experience working with data warehouses used by reporting/business teams Experience using Git/GitHub for version control in collaborative environments Understanding of data engineering best practices: Pipeline orchestration and dependency management Data quality, validation, and monitoring fundamentals Hands-on data migration experience, including: Schema mapping, transformation, and migration of legacy systems to new platforms Preserving business keys and maintaining data integrity throughout migration activities Data reconciliation and validation between source and target systems Identifying data inconsistencies, lineage gaps, and potential migration risks Performing data quality checks, integrity validation, and issue resolution Creating exception reports, control outputs, and auditable migration documentation Managing backfills and historical data handling Supporting cutover planning, execution, and post-migration validation Required for automation-focused role: Experience building and maintaining automated data pipelines: Scheduling, orchestration, and failure handling Workflow reliability, monitoring, and repeatability Preferred: Experience within Microsoft Fabric or broader Microsoft Azure data ecosystem Experience with orchestration tools such as Apache Airflow, Azure Data Factory, or Fabric pipelines Experience with data quality and observability practices: Validation frameworks, alerting, SLAs, monitoring Experience optimizing performance in data environments: Query tuning, partitioning, indexing, cost optimization Familiarity with modern data formats and large-scale processing: Parquet, Delta, incremental processing patterns Experience integrating with external systems: REST APIs, authentication, retries, error handling Exposure to CI/CD practices for data workflows: Git-based deployments, PR workflows, environment promotion Key Soft Skills:

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Data Engineering Analyst