Data Engineer

Description

Who We Are - Company Overview

CSC Leasing is a $1.3 billion equipment financing company purpose-built for the innovation economy. We partner with venture capital and private equity-backed companies across capital-intensive industries—from advanced manufacturing and life sciences to aerospace and industrial automation—providing the flexible financing solutions that high-growth companies need to scale without diluting their equity.

Unlike traditional lenders who shy away from emerging technologies and pre-profitability businesses, we lean in, underwriting the future by combining deep sector expertise with a venture-informed credit philosophy. Our team operates at the intersection of finance and innovation, evaluating leading-edge technologies and bold business models every single day.

If you thrive on intellectual curiosity, want a front-row seat to the companies reshaping entire industries, and believe that smart capital can be a catalyst for transformational change, CSC is where you belong.

Overview of role:

As a Data Engineer, you will build and maintain the data foundation that enables CSC to turn decades of 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 CSC's enterprise systems and historical data sources.

You will help create a trusted, governed, and accessible enterprise data environment spanning the full lifecycle of CSC's business - from origination and credit decisioning through funding, portfolio performance, asset management, end-of-lease activity, workouts, and ultimate economic outcomes. This is a highly hands-on engineering role that combines strong data engineering fundamentals with curiosity about the business and the ability to transform fragmented operational data into durable enterprise data assets that support analytics, data science, AI, and intelligent applications.

Role Impact

Success in this position means:

· CSC's historical and current data becomes connected, governed, trusted, and reusable.

· Data from Salesforce, NetSuite, AWS, legacy systems, documents, and other enterprise sources can be analyzed together.

· CSC can connect historical business decisions with actual economic outcomes.

· Data scientists and analysts spend less time finding, cleaning, and reconciling data and more time generating insights.

· Applications and other CSC agentic applications have secure access to high-quality, well-structured enterprise data.

· Data quality, lineage, documentation, and governance become embedded in the data platform rather than handled manually.

· CSC builds a proprietary data intelligence capability that becomes more valuable as additional history and operating data are incorporated.

Team Dynamics

· Collaborative Environment: Work closely with the Director, Innovation & Data Science, the Innovation Team, CSC's Senior Platform Engineer, and business stakeholders across origination, credit, portfolio, and asset management.

· Innovation Team Role: Be an integral part of CSC's Innovation Department, building the enterprise data foundation that powers analytics, data science, and AI-enabled applications.

Reporting Structure: Report directly to the Director, Innovation & Data Science, ensuring alignment with CSC's data and AI strategy.

Requirements

Key Responsibilities

Data Engineering & Pipelines

· 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 data ingestion, transformation, validation, reconciliation, and monitoring.

· Design pipelines that support both current operational data and large-scale ingestion of CSC's historical data.

· Optimize data pipelines for reliability, performance, maintainability, and cost.

Enterprise Data Modeling

· 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.

· Work with business stakeholders to understand how data is created, used, and interpreted across CSC.

· Create curated datasets and reusable data products for analytics, reporting, data science, AI, and operational applications.

· Support entity resolution and master-data practices necessary to establish consistent customer, deal, lease, and asset identities across systems.

Historical Data & Institutional Intelligence

· Support ingestion and normalization of CSC's historical data, including legacy credit, deal, asset, portfolio, and operational information.

· Work with the Director, Innovation & Data Science to connect historical decisions and assumptions with subsequent business and economic outcomes.

· Structure historical information so it can be used 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.

Data Quality, Governance & Observability

· Implement automated data-quality controls, reconciliation processes, testing, and monitoring.

· Establish technical lineage and documentation for critical enterprise data.

· Help implement CSC's data governance standards, access controls, retention requirements, and data classifications.

· Identify data-quality issues at their source and work with system owners to develop sustainable solutions rather than downstream workarounds.

· Develop metrics and monitoring to provide visibility into data freshness, completeness, accuracy, and pipeline health.

Analytics, Data Science & AI Enablement

· 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 required for predictive and prescriptive models.

· Prepare enterprise data for secure use by Lex, MCP-based services, agentic applications, and other AI-enabled systems.

· Support retrieval, analytical, and machine-learning workloads while maintaining appropriate governance and access controls.

· Evaluate new data technologies and capabilities that can improve CSC's ability to leverage its proprietary information.

Cloud & Platform Collaboration

· Partner closely 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.

· Maintain clear ownership between data engineering and platform engineering while jointly ensuring CSC's data workloads are secure, scalable, observable, and production-ready.

· Apply modern software-engineering and DevOps practices to data pipelines, transformations, testing, and deployment.

Qualifications

· Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Data Science, or 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 integrating SaaS/enterprise systems; Salesforce and/or NetSuite experience is valuable but not required.

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