Data Engineer

CSC Leasing Co. • Richmond, VA

4d

About The Position

As a Data Engineer, you will build and maintain the data foundation that enables CSC to turn its proprietary business and operating history into actionable intelligence. You will design, build, and maintain scalable data pipelines, models, and data services that connect information across enterprise systems and historical sources. The role supports a governed, accessible enterprise data environment spanning CSC’s business lifecycle, from origination and credit decisioning through funding, portfolio performance, asset management, end-of-lease activity, workouts, and economic outcomes.

This is a hands-on engineering role combining data engineering fundamentals, business curiosity, and the ability to transform fragmented operational data into durable enterprise data assets for analytics, data science, AI, and intelligent applications. You will work within CSC’s Innovation Department with the Director, Innovation & Data Science, the Innovation Team, the Senior Platform Engineer, and stakeholders across origination, credit, portfolio, and asset management. The position reports directly to the Director, Innovation & Data Science.

Success means connecting and governing historical and current data; enabling analysis across systems such as Salesforce, NetSuite, AWS, and legacy sources; linking historical business decisions to economic outcomes; reducing data preparation work for analysts and data scientists; providing secure access to high-quality data for applications; and embedding quality, lineage, documentation, and governance in the data platform.

Requirements

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Data Science, or a related field, or equivalent practical experience.
• 3–5+ years of professional data engineering experience.
• Experience designing and operating production ETL/ELT pipelines.
• Experience with AWS and modern cloud data architectures, including PostgreSQL and object storage such as S3.
• Experience with Databricks, Snowflake, Spark, Delta Lake, dbt, or similar modern data technologies.
• Experience implementing automated data-quality testing, monitoring, and reconciliation.
• Strong hands-on proficiency in SQL and Python for data processing, transformation, testing, and automation.
• Strong understanding of relational, analytical, dimensional, and modern data-modeling approaches.
• Experience integrating data from enterprise applications, databases, APIs, files, and third-party sources.
• Understanding of data lineage, metadata, data quality, access controls, master data, and governance principles.
• Experience with Git, automated testing, CI/CD concepts, and production software-development practices.
• Ability to understand the meaning and business context behind data.
• Familiarity with the data requirements of analytics, machine learning, LLMs, RAG, and AI applications.

Nice To Haves

• Experience integrating SaaS or enterprise systems; Salesforce and/or NetSuite experience is valuable but not required.

Responsibilities

• Design, build, test, and maintain reliable data pipelines across enterprise systems and data sources.
• Develop ingestion and transformation processes for structured and unstructured data from Salesforce, NetSuite, AWS PostgreSQL, S3, legacy systems, APIs, and other sources.
• Build automated processes for incremental ingestion, transformation, validation, reconciliation, and monitoring.
• Design pipelines for operational data and large-scale historical-data ingestion, and optimize them for reliability, performance, maintainability, and cost.
• Develop and maintain canonical enterprise data models connecting customers, opportunities, deals, credit decisions, leases, assets, payments, portfolio activity, end-of-lease events, workouts, recoveries, and other business information.
• Establish durable relationships between historical and current records across systems, and work with business stakeholders to understand how data is created, used, and interpreted.
• Create curated datasets and reusable data products for analytics, reporting, data science, AI, and operational applications.
• Support entity resolution and master-data practices for consistent customer, deal, lease, and asset identities across systems.
• Ingest and normalize historical credit, deal, asset, portfolio, and operational data.
• Work with the Director, Innovation & Data Science to connect historical decisions and assumptions with subsequent business and economic outcomes.
• Structure historical data for longitudinal analysis, predictive modeling, comparable-deal analysis, portfolio risk management, and AI-enabled decision support.
• Preserve source attribution and lineage between original information, extracted data, transformations, and curated datasets.
• Implement automated data-quality controls, reconciliation processes, testing, and monitoring.
• Establish technical lineage and documentation for critical enterprise data, and help implement governance standards, access controls, retention requirements, and data classifications.
• Identify data-quality issues at their source and work with system owners on sustainable solutions.
• Develop metrics and monitoring for data freshness, completeness, accuracy, and pipeline health.
• Build trusted datasets and data services supporting Power BI, advanced analytics, predictive models, portfolio analysis, and data-science initiatives.
• Partner with the Director, Innovation & Data Science to productionize data pipelines and features required for predictive and prescriptive models.
• Prepare enterprise data for secure use by Lex, MCP-based services, agentic applications, and other AI-enabled systems while supporting retrieval, analytical, and machine-learning workloads with appropriate governance and access controls.
• Evaluate data technologies and capabilities that can improve CSC’s use of its proprietary information.
• Partner with the Senior Platform Engineer on the infrastructure supporting the enterprise data platform.
• Collaborate on AWS architecture, security, IAM, infrastructure-as-code, CI/CD, monitoring, deployment standards, and production reliability.
• Apply modern software-engineering and DevOps practices to data pipelines, transformations, testing, and deployment.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level