Senior Data Engineer

Based in New Jersey and established in 2010, Asset Based Lending, LLC (“ABL”) is one of the nation’s leading Hard Money Lenders. We provide fast bridge financing to real estate investors for the purchase, renovation, or new construction of single family, multi-family and mixed-use properties as well as DSCR rental loans for long term investors. We have closed thousands of loans since we began lending fourteen years ago, producing nearly $3B in originations. ABL was acquired by a private equity firm in 2021, and with a renewed focus on growth, we’ve set our sights on consistent evolution and cementing our place as the premier private lender in the country. Our Mission is Simple: Make Good Loans Provide Exceptional Service, Every Time Protect The Firm Build The Future Position Overview We are seeking a Senior Data Engineer to own and evolve our enterprise data platform, enabling trusted analytics, regulatory reporting, and AI-ready data at scale. This role will lead the design, implementation, and optimization of our new data lake and data warehouse, integrating multiple internal and external source systems while ensuring strong standards for data quality, governance, security, and reliability. This is a hands‑on individual contributor role with significant architectural ownership and technical decision‑making authority. The Senior Data Engineer will work closely with Analytics, AI/Data Science, and business stakeholders to translate complex business and regulatory requirements into scalable, compliant, and high‑performance data solutions that support ABL’s growth and operational excellence. Key Responsibilities Own end‑to‑end design, implementation, and evolution of enterprise data pipelines and core data domains, from source ingestion through analytics and AI‑ready datasets Architect, develop, and optimize scalable ETL/ELT pipelines integrating multiple internal and external source systems Lead the design and optimization of the data lake and data warehouse to support analytics, regulatory reporting, and operational decision‑making Define and enforce standards for data modeling, testing, deployment, and documentation to ensure long term scalability and maintainability Implement and maintain data quality, reliability, and observability practices, including automated testing, monitoring, and alerting Establish and support data governance, metadata management, lineage, and role‑based access controls in partnership with business and compliance stakeholders Design and maintain analytics and ML‑ready datasets to support BI, advanced analytics, and future AI/ML initiatives Apply DevOps and DataOps best practices, including CI/CD, version control, and environment management for data pipelines Troubleshoot and resolve complex data issues involving legacy systems, custom integrations, and evolving business requirements Partner closely with Analytics, AI/Data Science, and business leaders to translate complex business and regulatory requirements into robust technical solutions Provide technical guidance, code reviews, and best practices to junior data engineers and analysts, contributing to a high‑quality data engineering practice (no direct people management) Create and maintain clear, comprehensive documentation for data models, pipelines, architectures, and governance processes to support scalability, knowledge sharing, and operational continuity Experience & Seniority Extensive professional experience in data engineering or related roles Demonstrated experience owning and operating production‑grade data platforms in a cloud environment Proven experience designing and scaling data lakes and data warehouses supporting analytics, reporting, and business critical use cases Strong ability to translate complex business and regulatory requirements into reliable, maintainable data solutions Experience operating autonomously as a senior individual contributor with accountability for architecture, quality, and delivery Senior individual contributor role with no direct people management responsibilities This role reports to the Analytics & AI/Data Science Lead Education & Certifications Bachelor’s degree in computer science, Engineering, or a related quantitative field Certifications are a plus, and experience with the following cloud platforms: AWS, Azure, or Google Cloud Platform Data warehousing or analytics certifications Technical Skills & Proficiency Programming & Analytics Strong proficiency in Python, including PySpark, for data engineering, automation, and pipeline development Expert‑level SQL for analytical modeling, performance tuning, and data warehouse optimization Deep experience with dbt for transformation, testing, and analytics engineering Experience supporting BI tools such as Power BI or similar analytics platforms Data & Platform Technologies Hands‑on experience designing and operating modern cloud data platforms (Snowflake, Databricks, and/or Microsoft Fabric) Experience building and managing ELT pipelines using tools such as Fivetran, Airbyte, or equivalent technologies Experience implementing data governance, metadata management, lineage, and access controls using tools such as Collibra or Microsoft Purview Strong foundation in data modeling (dimensional, analytical, and domain‑oriented models) Experience implementing data quality, testing, and observability practices for production data pipelines Familiarity with CI/CD, version control, and Infrastructure‑as‑Code concepts applied to data platforms Solid understanding of security, privacy, and access control considerations in enterprise data environments Scope & Impact Significant ownership and technical decision‑making authority over the enterprise data platform, including architecture, tooling, standards, and implementation approaches Direct impact on the reliability, scalability, and compliance of data used for analytics, regulatory reporting, and business decision‑making Acts as a key technical partner to Analytics, AI/Data Science, and business stakeholders, influencing data strategy and execution Responsible for balancing hands‑on delivery with architectural thinking, scalability planning, and long‑term platform sustainability Plays a critical role in establishing and maintaining data quality, governance, security, and access control standards across the organization #J-18808-Ljbffr