Project description
We are looking for a Data Engineer with strong hands‑on expertise in building scalable data platforms to support mortgage and lending systems. The role involves working on loan origination, servicing, and risk analytics data, enabling reporting, regulatory compliance, and business insights, using technologies including Apache Spark, Python, and BI tools such as Amazon QuickSight.
Responsibilities
* Design, build, and maintain data pipelines supporting mortgage systems, including origination, underwriting, and servicing.
* Process large-scale datasets using Apache Spark, with a preference for PySpark.
* Develop clean, scalable, and efficient Python code.
* Integrate data from multiple sources such as loan systems, credit bureaus, and third‑party providers.
* Build and optimize ETL/ELT workflows for batch and near real‑time processing.
* Develop datasets and dashboards using Amazon QuickSight or comparable tools for mortgage reporting and key performance indicators.
* Support regulatory and compliance reporting related to loan performance and risk exposure.
* Ensure data quality, lineage, and governance across mortgage data platforms.
* Collaborate with business stakeholders in risk, underwriting, and operations to translate requirements into data solutions.
* Optimize the performance and scalability of data pipelines and storage systems.
SKILLS
Must have
* At least 6 years of experience in Data Engineering space.
* Strong experience building and maintaining data pipelines for banking data domains.
* Hands‑on expertise with Apache Spark / PySpark for large‑scale data processing.
* Strong Python development skills with emphasis on reusable, efficient code.
* Solid ETL/ELT engineering experience (ingestion, transformation, integration).
* Experience supporting data modelling for reporting/analytics use cases.
* Exposure to BI/dashboarding tools such as Amazon Quicksight, Power BI, Tableau (or similar).
* Practical experience with data quality, governance, and working in regulated environments.
Nice to have
N/A
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