Data Engineer - Remote

# Director, Data Engineering – Data & AI Platform

## About the Role

Kapitus is building a next-generation enterprise Data & AI capability through a multi-year modernization program focused on replacing legacy analytics workflows with governed, reusable business data products on a modern cloud data platform, establishing an AI/ML platform, and enabling the organization to move from producing reports to producing decisions.

The Director of Data Engineering will serve as the senior technical leader for this transformation, owning how the platform is engineered. This player-coach role will define architecture and engineering standards for a Snowflake-centered data platform, lead internal and partner engineering teams across onshore and offshore locations, and remain hands-on with code, designs, data models, pipelines, performance, and technical problem solving. The role reports directly to the program executive leading the Data & AI organization and serves as the technical delivery lead alongside the Technical Program Manager.

## Key Responsibilities

### Data Platform Architecture & Engineering

• * Own the engineering and solution architecture of the data platform within approved enterprise and data architecture standards.
• * Define and maintain layered warehouse architecture, data modeling, ingestion and transformation patterns, orchestration, environment strategy, and promotion controls.
• * Translate approved data architecture, MDM, ontology, semantic, and data-contract standards into enforceable engineering patterns.
• * Establish and enforce standards for repository structure, branching and CI/CD, code review, testing, data quality, naming, documentation, performance, and cost management.
• * Lead design reviews and architecture decision forums, document technical decisions, manage exceptions, and prevent conformance debt.
• * Design reusable ingestion frameworks, certified transformation patterns, common serving structures, and semantically consistent data products.
• * Remain technically hands-on by reviewing critical code and designs, prototyping high-risk components, tuning warehouse performance and cost, and directly resolving technical blockers.

### Data Products & Modernization

• * Lead the engineering delivery of governed business data products from source onboarding through ELT pipeline development, data modeling, quality controls, serving, consumption, and production release.
• * Direct migration and decommissioning of legacy analytics workflows onto the modern data platform.
• * Evaluate whether legacy workflows should be reused, refactored, retired, or rebuilt based on technical and business analysis.
• * Own platform hardening and readiness, including environments, security and access patterns, orchestration reliability, and operational readiness.
• * Establish production reliability practices covering observability, service-level objectives, incident and problem management, recovery patterns, runbooks, and resilience/continuity requirements.
• * Drive recurring production failures to root-cause resolution.

### AI/ML Platform Engineering

• * Engineer the data foundations required for AI/ML initiatives, including feature-ready data and ML pipeline integration.
• * Support the data requirements of GenAI and agentic AI workloads, including retrieval foundations, training and inference data flows, and monitoring.
• * Synchronize AI/ML data foundations with overall platform readiness rather than treating them as separate or later-stage capabilities.

### Program Delivery

• * Deliver against program milestone gates, including vendor verification, business/customer validation, and production acceptance.
• * Ensure engineering work is validated, evidenced, and accepted rather than considering code completion the final delivery milestone.
• * Partner with the Technical Program Manager on sequencing, capacity, dependencies, and gate readiness.
• * Own technical feasibility and engineering quality while the TPM owns the integrated program plan and commercial controls.

### Team Leadership

• * Lead and develop a blended engineering organization consisting of internal engineers, partner delivery pods, and offshore teams across time zones.
• * Establish effective onshore/offshore collaboration through clear handoffs, detailed design specifications, deliberate overlap windows, and consistent quality standards.
• * Maintain separation of duties between engineering build and validation teams.
• * Assess partner engineering quality through direct review of designs and code and hold delivery partners to the same standards as internal teams.
• * Hire, coach, and develop engineers while creating a culture where issues surface early and evidence drives decisions.

### Cross-Functional Partnership

• * Partner with Data Governance to embed quality rules, lineage, classification handling, and catalog readiness directly into data pipelines.
• * Work directly with business owners and architecture teams to explain technical trade-offs and drive timely decisions.
• * Communicate technical strategy and architecture effectively to engineers, executives, and other stakeholders.

## Required Qualifications

### Experience & Leadership

• * 10+ years of data engineering experience.
• * 5+ years leading data engineering teams through enterprise data platform builds or modernization programs.
• * Demonstrated technical depth in engineering leadership and hands-on architecture, such as experience as a Principal Engineer, Lead Architect, Consulting Delivery Lead, Engineering Manager, or similar role.
• * Proven experience leading distributed onshore/offshore engineering teams, including partner and vendor teams.

### Snowflake & Data Engineering

* Deep hands-on Snowflake expertise, including:

• * Warehouse and database design
• * Performance tuning
• * Cost and workload optimization
• * Security and access patterns
• * Roles, masking, and row-level security
• * Data sharing
• * Production-scale operational administration
• * Hands-on expertise with dbt, including:

• * Project architecture
• * Layered modeling conventions
• * Testing and documentation
• * Macros and packages
• * CI/CD integration
• * Multi-team and multi-environment deployments
• * Managing project, model, and dependency sprawl
• * Strong knowledge of modern data engineering stacks, including orchestration, ingestion, CDC, streaming, batch processing, data catalogs, lineage, semantic/BI layers, and legacy workflow migration.
• * Expert-level SQL skills.
• * Strong Python experience.
• * Experience with Git-based development workflows, CI/CD, automated testing, and infrastructure-as-code awareness.
• * Strong understanding of enterprise-scale data modeling and layered data architectures.

### AI/ML & Platform Engineering

• * Understanding of the data platform requirements of ML and GenAI workloads, including feature pipelines, training and inference data flows, retrieval foundations, and monitoring.
• * Ability to engineer data platforms that support AI/ML initiatives and lead teams delivering these capabilities.

### Communication & Delivery

• * Strong architecture and technical decision-making skills.
• * Ability to defend architectural decisions to engineers and explain technical trade-offs to executives and business stakeholders.
• * Strong technical documentation and communication skills.
• * Experience delivering engineering programs against defined acceptance criteria, timelines, and quality standards.

## Preferred Qualifications

• * Experience in financial services, including lending, banking, fintech, or another regulated environment.
• * Experience working under data governance, model risk, and audit requirements.
• * SnowPro certifications, including Core, Advanced Architect, or Data Engineer.
• * dbt certification.
• * AWS, Azure, or other cloud certifications.
• * Databricks experience.
• * Consulting or professional services delivery experience, including client-facing engineering delivery under tight timelines, commercial constraints, and contractual milestone commitments.
• * Experience with data product operating models, including data mesh, data product thinking, data contracts, and certified metrics.
• * FinOps experience managing cloud data platform costs as an engineering discipline.
• * Experience establishing engineering practices from scratch, including standards, CI/CD, code review culture, validation streams, and operational readiness.
• * Experience with orchestration technologies such as Airflow or Dagster.
• * Experience with ingestion and CDC tooling, streaming and batch architectures, data catalogs, lineage, semantic/BI layers, and legacy technologies such as Alteryx or SSIS.

## Location & Work Arrangement

• * **Primary location:** Arlington, VA.
• * Qualified remote candidates may be considered if they reside in states where Kapitus and/or one of its subsidiaries has an established physical presence.

## Compensation

• * **Base Salary Range:** $157,100–$252,000
• * **Annual Incentive Compensation:** Eligible for up to 15% annually.
• * Final salary will depend on factors including geographic location, skills, and experience.

## Benefits

Kapitus offers a comprehensive benefits package that includes:

• * Medical, dental, and employer-paid vision insurance through UnitedHealthcare (UHC).
• * Flexible Spending Account (FSA).
• * Lifestyle Spending Account.
• * Company-paid basic short-term and long-term disability insurance.
• * Voluntary supplemental life and disability insurance.
• * Colonial Accident and Hospitalization insurance options.
• * Paid maternity and parental leave.
• * Pre-tax commuter benefits.
• * LifeBalance program and associated lifestyle discounts.
• * Pet and car insurance discounts.
• * LegalShield financial and legal services.
• * Plum Benefits discounts for entertainment, travel, and car rentals.
• * Tuition reimbursement of up to $5,000 annually.
• * Conference and career development opportunities through Kapitus Academy.
• * Work-related travel reimbursement.
• * Paid time off and sick time.
• * 401(k) through Fidelity with a 25% company match on employee contributions, up to 6% of annual salary.

## About Kapitus

Kapitus is a small business financing company that operates as both a direct lender and a marketplace supported by a network of lending partners. The company provides small businesses with financing solutions designed around their individual needs.

Kapitus' mission is to help small business owners grow through tailored, transparent, and ethical financing solutions. The company emphasizes transparency, fairness, integrity, strong client relationships, and putting the best interests of business owners at the center of the financing process.

## Recruiting & Candidate Safety

Kapitus will never ask candidates for payment or sensitive financial information during the initial application or interview process. Candidates should use Kapitus' legitimate employment portals when applying for open positions.

Questions or concerns regarding recruiting communications or the recruiting process can be directed to [[email protected]](mailto:[email protected]).