Data Engineer

CSC Leasing Co.•Richmond, VA

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 connecting information across CSC’s enterprise systems and historical data sources. This hands-on engineering role combines data engineering fundamentals with business curiosity to transform fragmented operational data into durable enterprise assets for analytics, data science, AI, and intelligent applications. The role focuses on creating a trusted, governed, and 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. You will work with the Director, Innovation & Data Science, the Innovation Team, CSC’s Senior Platform Engineer, and stakeholders across origination, credit, portfolio, and asset management. The position reports directly to the Director, Innovation & Data Science and is part of CSC’s Innovation Department.

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 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 business meaning and context behind data.

Nice To Haves

Experience integrating SaaS or enterprise systems; Salesforce and/or NetSuite experience is valuable but not required.
Familiarity with the data requirements of analytics, machine learning, LLMs, RAG, and AI applications.
Experience with AWS services such as S3, PostgreSQL/RDS/Aurora, and related cloud data technologies.

Responsibilities

Design, build, test, and maintain reliable data pipelines across CSC’s 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 current operational data and large-scale ingestion of historical data; 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 to establish consistent customer, deal, lease, and asset identities across systems.
Support ingestion and normalization of 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 information 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.
Help implement data 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 freshness, completeness, accuracy, and pipeline health.
Build trusted datasets and data services supporting Power BI, advanced analytics, predictive models, portfolio analysis, and other data-science initiatives.
Partner with the Director, Innovation & Data Science to productionize data pipelines and features for predictive and prescriptive models.
Prepare enterprise data for secure use by Lex, MCP-based services, agentic applications, and other AI-enabled systems, maintaining appropriate governance and access controls for retrieval, analytical, and machine-learning workloads.
Evaluate data technologies and capabilities that can improve CSC’s use of its proprietary information.
Partner with CSC’s 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 while maintaining clear ownership with platform engineering.