CreditXpert
Overview
Data Engineer role at CreditXpert. The position focuses on designing, building, and optimizing data pipelines and infrastructure to power analytics and decision‑making across the organization. This role ensures data is reliable, scalable, and ready for analysts, data scientists, and business stakeholders. What you will do
Design and maintain robust data transformation pipelines –
build scalable transformation workflows in dbt that transform raw data into clean, analysis-ready tables. Apply best practices so data is trustworthy and reusable. Enable analytics and insights –
prepare and optimize data for analysts, business intelligence, machine learning applications, and reverse ETL. Ensure reliability and scalability –
monitor, test, and improve the performance and quality of data infrastructure. How you will do it
Engineer with a balance of systems and product mindsets
– design pipelines and workflows that are efficient, resilient, and maintainable, adapt to evolving business needs. Prioritize data quality and consistency
– implement checks and governance to ensure trustworthy analytics. Collaborate cross-functionally
– work with analytics engineers, BI developers, product, marketing, and sales teams to translate requirements into scalable data solutions. Essential Functions
Develop and maintain dbt models that transform raw data into trusted, reusable datasets. Apply best practices in design, testing, and documentation to ensure reliability. Deliver analysis-ready tables, ensuring consistency across business metrics. Build and manage ETL/ELT pipelines using modern orchestration tools. Design, document, and optimize schemas and data models for analytics workloads. Implement data quality checks, monitoring, lineage tracking, anomaly detection, and alerting for pipelines. Develop and maintain cloud-based data infrastructure. Automate data workflows to reduce manual processes and errors. Optimize performance of data queries and warehouse workloads. Collaborate with analysts and data scientists to deliver clean, ready-to-use data. Support incident resolution and root-cause analysis for data issues. Stay current with best practices in data engineering and modern data stack tools. Required Education and Experience
4 – 6 years of experience in data engineering, analytics engineering, or related roles. 4+ years with data models, data quality assurance, and ELT/ETL systems. Experience designing data models to enable business intelligence and analytics. Familiarity with reverse ETL. BS/BA degree in Engineering, Computer Science, Information Technology, or a related technical field. Advanced proficiency with SQL and experience with SQL database design in a business setting. Experience with SQL-based data transformation tools (e.g. dbt, Coalesce, SQLMesh, Dataform). Strong knowledge of ETL/ELT practices and cloud data warehouse technologies (Snowflake, BigQuery, Redshift, Databricks). Proficiency with at least one programming language (Python, Scala, or Java). Familiarity with distributed data processing frameworks (e.g., Spark, Flink). Comfort with Git-based workflows and CI/CD practices. Familiarity with data science and machine learning concepts. Familiarity with Agile Scrum frameworks and Product Management tools such as Atlassian Jira and Confluence. Experience documenting datasets and business logic for shared understanding. Understanding of data governance/security practices. Exposure to cost/performance optimization in a cloud data platform. Competencies and Success Criteria
Data pipeline development
– ability to design and implement scalable transformation pipelines. Data modeling
– ability to structure data for analytical performance and usability. Data quality & governance
– ability to enforce accuracy, consistency, and reliability. Problem-solving
– ability to diagnose and resolve data and system performance issues. Cross-functional collaboration
– ability to partner effectively with analytics and business teams. Position Details
Seniority level:
Mid-Senior level Employment type:
Full-time Travel:
0% About CreditXpert
CreditXpert Inc. is a successful software company with over 20 years in business. We are a small, close-knit team focused on meaningful work and making a positive impact by improving Americans’ financial lives. We value continuous learning, adaptability, and a culture that supports autonomy, collaboration, and growth. What we do We started in 2000 by exposing the inner workings of the credit scoring industry to consumers, pioneering a new industry. We enable consumers to take control of their credit life, gain access to better loans, and save money. We also support mortgage origination by using technology to improve credit scores and help professionals serve clients more effectively. Tech Stack
Systems + Tools Snowflake AWS Atlassian: Jira and Confluence Familiarity with machine learning frameworks (e.g., Keras, PyTorch) and libraries (e.g., scikit-learn) Familiarity with Mathematical Optimization Bitbucket and CI/CD Sigma Boomi dbt Cloud Hightouch HubSpot Bitbucket and CI/CD Languages SQL Optional: Rust, Java, C++, R, SAS, Stata, Matlab, JMP Benefits
Flexible and hybrid work environment Open PTO Performance-based annual bonuses Company contributions to 401(k) Insurance (medical, dental, vision, ST/LT disability, life) HSA and FSA
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Data Engineer role at CreditXpert. The position focuses on designing, building, and optimizing data pipelines and infrastructure to power analytics and decision‑making across the organization. This role ensures data is reliable, scalable, and ready for analysts, data scientists, and business stakeholders. What you will do
Design and maintain robust data transformation pipelines –
build scalable transformation workflows in dbt that transform raw data into clean, analysis-ready tables. Apply best practices so data is trustworthy and reusable. Enable analytics and insights –
prepare and optimize data for analysts, business intelligence, machine learning applications, and reverse ETL. Ensure reliability and scalability –
monitor, test, and improve the performance and quality of data infrastructure. How you will do it
Engineer with a balance of systems and product mindsets
– design pipelines and workflows that are efficient, resilient, and maintainable, adapt to evolving business needs. Prioritize data quality and consistency
– implement checks and governance to ensure trustworthy analytics. Collaborate cross-functionally
– work with analytics engineers, BI developers, product, marketing, and sales teams to translate requirements into scalable data solutions. Essential Functions
Develop and maintain dbt models that transform raw data into trusted, reusable datasets. Apply best practices in design, testing, and documentation to ensure reliability. Deliver analysis-ready tables, ensuring consistency across business metrics. Build and manage ETL/ELT pipelines using modern orchestration tools. Design, document, and optimize schemas and data models for analytics workloads. Implement data quality checks, monitoring, lineage tracking, anomaly detection, and alerting for pipelines. Develop and maintain cloud-based data infrastructure. Automate data workflows to reduce manual processes and errors. Optimize performance of data queries and warehouse workloads. Collaborate with analysts and data scientists to deliver clean, ready-to-use data. Support incident resolution and root-cause analysis for data issues. Stay current with best practices in data engineering and modern data stack tools. Required Education and Experience
4 – 6 years of experience in data engineering, analytics engineering, or related roles. 4+ years with data models, data quality assurance, and ELT/ETL systems. Experience designing data models to enable business intelligence and analytics. Familiarity with reverse ETL. BS/BA degree in Engineering, Computer Science, Information Technology, or a related technical field. Advanced proficiency with SQL and experience with SQL database design in a business setting. Experience with SQL-based data transformation tools (e.g. dbt, Coalesce, SQLMesh, Dataform). Strong knowledge of ETL/ELT practices and cloud data warehouse technologies (Snowflake, BigQuery, Redshift, Databricks). Proficiency with at least one programming language (Python, Scala, or Java). Familiarity with distributed data processing frameworks (e.g., Spark, Flink). Comfort with Git-based workflows and CI/CD practices. Familiarity with data science and machine learning concepts. Familiarity with Agile Scrum frameworks and Product Management tools such as Atlassian Jira and Confluence. Experience documenting datasets and business logic for shared understanding. Understanding of data governance/security practices. Exposure to cost/performance optimization in a cloud data platform. Competencies and Success Criteria
Data pipeline development
– ability to design and implement scalable transformation pipelines. Data modeling
– ability to structure data for analytical performance and usability. Data quality & governance
– ability to enforce accuracy, consistency, and reliability. Problem-solving
– ability to diagnose and resolve data and system performance issues. Cross-functional collaboration
– ability to partner effectively with analytics and business teams. Position Details
Seniority level:
Mid-Senior level Employment type:
Full-time Travel:
0% About CreditXpert
CreditXpert Inc. is a successful software company with over 20 years in business. We are a small, close-knit team focused on meaningful work and making a positive impact by improving Americans’ financial lives. We value continuous learning, adaptability, and a culture that supports autonomy, collaboration, and growth. What we do We started in 2000 by exposing the inner workings of the credit scoring industry to consumers, pioneering a new industry. We enable consumers to take control of their credit life, gain access to better loans, and save money. We also support mortgage origination by using technology to improve credit scores and help professionals serve clients more effectively. Tech Stack
Systems + Tools Snowflake AWS Atlassian: Jira and Confluence Familiarity with machine learning frameworks (e.g., Keras, PyTorch) and libraries (e.g., scikit-learn) Familiarity with Mathematical Optimization Bitbucket and CI/CD Sigma Boomi dbt Cloud Hightouch HubSpot Bitbucket and CI/CD Languages SQL Optional: Rust, Java, C++, R, SAS, Stata, Matlab, JMP Benefits
Flexible and hybrid work environment Open PTO Performance-based annual bonuses Company contributions to 401(k) Insurance (medical, dental, vision, ST/LT disability, life) HSA and FSA
#J-18808-Ljbffr