Data Integration and Governance Analyst

World Business Lenders

3d•Remote

About The Position

World Business Lenders provides flexible, short-term commercial loans backed by real estate to small and medium-sized businesses across the United States, particularly those facing difficulties with traditional financing. This contract/consultant position focuses on improving automated data pipelines and migrating legacy systems. The analyst will help ensure accurate, consistent, and reliable data throughout processing workflows while reducing manual intervention and errors, and will support thorough analysis and seamless data transfer and integration to improve system performance and maintain continuity of operations. The role is fully remote, with typical working hours of 9:00 a.m. to 6:00 p.m. Eastern Time, Monday through Friday. Applicants must have excellent spoken and written English, submit a CV in English, and maintain a stable internet connection and professional remote working setup.

Requirements

Four to seven years of experience as a Data Engineer.
Experience with cloud-based data platforms and environments.
Strong SQL skills and data-modeling experience for data warehouses.
Strong Python skills, especially using notebooks, for building and maintaining data workflows.
Experience developing and maintaining ETL/ELT processes.
Experience with data warehouses used by reporting and business teams.
Experience using Git/GitHub for version control in collaborative environments.
Understanding of data engineering best practices, including pipeline orchestration, dependency management, data quality, validation, and monitoring.
For migration-focused work: hands-on data migration experience, including schema mapping and transformation, strong data reconciliation and validation, data quality and integrity checks, issue resolution, backfills, historical data handling, and cutover planning and execution.
For automation-focused work: experience building and maintaining automated data pipelines, including scheduling, orchestration, failure handling, reliability, monitoring, and repeatability.
Clear communication skills for explaining data issues, tradeoffs, and results to technical and business stakeholders.
Ownership and accountability, including taking end-to-end responsibility for pipelines, data quality, and outcomes.
Problem-solving skills, including debugging complex data issues and working independently through ambiguity.
Ability to collaborate across engineering, analytics, and business teams.
Adaptability to evolving environments, changing requirements, and new tools.
Proficiency in SQL, Python, cloud technologies, and data competency.
Availability to work Monday through Friday, 9:00 a.m. to 6:00 p.m. Eastern Time.
Stable internet connection and a professional remote working setup.
CV/resume must be submitted in English.
Excellent command of spoken and written English.

Nice To Haves

Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field; equivalent hands-on experience is also valued.
Experience with Microsoft Fabric or the 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, including validation frameworks, alerting, SLAs, and monitoring.
Experience optimizing data-environment performance, including query tuning, partitioning, indexing, and cost optimization.
Familiarity with modern data formats and large-scale processing, including Parquet, Delta, and incremental processing patterns.
Experience integrating external systems, including REST APIs, authentication, retries, and error handling.
Exposure to CI/CD practices for data workflows, including Git-based deployments, pull-request workflows, and environment promotion.

Responsibilities

Improve data pipelines through automation and implement quality checks to ensure data accuracy, consistency, and reliability throughout data-processing workflows.
Reduce manual intervention and minimize errors in data processing.
Migrate legacy systems to new systems through analysis, data transfer, and integration, enhancing performance and maintaining continuity without disrupting existing operations.
Build and maintain automated data pipelines, including scheduling, orchestration, failure handling, monitoring, and repeatability.
Perform data migration tasks, including schema mapping and transformation, reconciliation and validation, integrity checks, issue resolution, backfills, historical data handling, and cutover planning and execution.