Remote - Data Engineering Analyst

World Business Lenders

6d•Remote

About The Position

About World Business Lenders (www.wbl.com) 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.

Requirements

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!
4 to 7 years of experience as a Data Engineer. We value practical, hands-on expertise that goes beyond just theoretical knowledge.
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: Schema mapping and transformation Data reconciliation and validation (must be strong here) Data quality checks, integrity validation, and issue resolution Backfills and historical data handling Cutover planning and execution
Experience building and maintaining automated data pipelines: Scheduling, orchestration, and failure handling Workflow reliability, monitoring, and repeatability
Clear communication: can explain data issues, tradeoffs, and results to both technical and business stakeholders.
Ownership & accountability: takes end-to-end responsibility for pipelines, data quality, and outcomes.
Problem-solving mindset: able to debug complex data issues and work through ambiguity independently.
Collaboration: works effectively across engineering, analytics, and business teams.
Adaptability: comfortable operating in evolving environments (migration, changing requirements, new tools).
Proficiency in SQL, Python, cloud technologies, and data competency.

Nice To Haves

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

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.