Interested in this Data Engineer role at Kapitus?
Apply Now →Skills & Technologies
About This Role
Attention: Kapitus is aware that individuals posing as recruiters may be communicating with job seekers about supposed positions with Kapitus. Kapitus has received reports that the content and method of communication can vary, but messages may contain requests for payment (e.g., fees for equipment or training) and/or for sensitive financial information.
Kapitus will never ask a candidate for employment for payment or financial information during the initial application or interview process. All open positions are posted in location specific employment portals available at www.kapitus.com/careersAll legitimate Kapitus job postings on employment sites will direct candidates to complete an application through these portals before completion of the hiring process.
Candidates with additional questions or concerns regarding any recruiting communications or Kapitus' recruiting process in general should email [email protected]
Kapitus is building a next-generation enterprise data and AI capability multi-year modernization program that replaces hundreds of legacy analytics workflows with governed, reusable business data products on a modern cloud data platform, stands up an AI/ML platform, and moves the organization from producing reports to producing decisions.
We are looking for a Director of Data Engineering to serve as the senior technical leader for this program: the person who owns how the platform is engineered. You will set the architecture and engineering standards for a Snowflake-centered data platform, lead internal and partner engineering teams across onshore and offshore locations, and stay hands-on enough to review a pull request, challenge a data model, and unblock a pipeline yourself. This is a player-coach role — you will spend real time in the code and the designs, not only in meetings.
You report directly to the program executive leading the Data & AI organization and serve as the technical delivery lead for the transformation program. You are the counterpart to the Technical Program Manager: the TPM owns the integrated plan, the gates, and the commercial controls; you own the architecture, the engineering quality, and the teams that build. Together you make sure what ships is well-built, evidenced, and accepted — not merely finished.
What you will do:
Architecture and technical leadership
- Own the engineering and solution architecture of the data platform within approved enterprise and data architecture standards including layered warehouse design, data modeling, ingestion and transformation patterns, orchestration, environment strategy, and promotion controls and keep delivery conformant to it.
- Translate approved data architecture, MDM, ontology, semantic, and data-contract standards into enforceable engineering patterns so identity, meaning, metrics, and relationships remain consistent across products.
- Set and enforce engineering standards: repository structure, branching and CI/CD, code review, testing and data quality checks, naming and documentation, and performance and cost discipline.
- Lead design reviews and architecture decision forums; document decisions, manage exceptions with owners and expiry dates, and prevent conformance debt from silently accumulating.
- Design for reuse: shared ingestion frameworks, certified transformation patterns, common serving structures, and semantic consistency — so each delivery wave gets faster, not just bigger.
- Stay technically hands-on: review critical code and designs, prototype or intervene on the highest-risk components when needed, tune warehouse performance and cost, and establish the engineering bar through direct technical engagement.
Engineering delivery
- Lead the engineering build of governed business data products end to end: source onboarding, ELT pipeline development, data modeling, quality controls, serving and consumption structures, and production release.
- Direct the migration and decommissioning of legacy analytics workflows onto the modern platform. Reuse, refactor, retire, or rebuild decisions grounded in analysis, not habit.
- Own platform hardening and readiness: environments, security and access patterns, orchestration reliability, and operational readiness the platform is a product, not a project by-product.
- Own engineering reliability for production data services including observability, service-level objectives, incident and problem management, recovery patterns, runbooks, and resilience/continuity requirements with recurring failures driven to root-cause closure.
- Engineer for the AI/ML workstream: feature-ready data, ML pipeline integration, and the data foundations that GenAI and agentic workloads depend on synchronized with platform readiness rather than bolted on.
- Deliver against the program's milestone gates: vendor verification, business/customer validation, production acceptance. Code complete is not product complete your teams' work is done when it is validated, evidenced, and accepted.
Team leadership — onshore and offshore
- Lead and develop a blended engineering organization: internal engineers, partner delivery pods, and offshore teams across time zones.
- Make the onshore/offshore model actually work: clean handoffs, clear design specifications before build starts, overlap windows used deliberately, and quality standards that do not vary by location.
- Uphold separation of duties between build and validation teams, and give validators what they need to verify work independently.
- Assess partner engineering quality directlyreview their designs and code, challenge estimates from technical knowledge, and hold delivery partners to the same standards as internal teams.
- Hire, coach, and grow engineers; set clear expectations; and build a culture where problems surface early and evidence beats assertion.
Cross-functional partnership
- Partner with the Technical Program Manager on sequencing, capacity, dependency management, and gate readiness you own technical feasibility and quality; the TPM owns the integrated plan and commercial controls.
- Work with data governance so controls ship with the product: quality rules, lineage, classification handling, and catalog readiness built into pipelines rather than retrofitted.
- Engage business owners and architecture directly — explain technical trade-offs in business terms and drive timely technical decisions with accountable owners.
What we're looking for:
- 10+ years of data engineering experience
- 5+ years leading data engineering teams through enterprise data platform builds or modernizations. Backgrounds that combine engineering leadership with hands-on architecture such as a principal engineer, lead architect, consulting delivery lead, engineering manager are strongly valued; demonstrated technical depth matters more than title progression alone.
- Deep hands-on Snowflake expertise with warehouse and database design, performance tuning, cost and workload optimization, security and access patterns (roles, masking, row-level security), data sharing, and operational administration at production scale.
- Hands-on dbt mastery including project architecture, layered modeling conventions, testing and documentation, macros and packages, CI/CD integration, and running dbt across multiple teams and environments while preventing uncontrolled project, model, and dependency sprawl.
- Broad modern data stack fluency with orchestration (e.g., Airflow, Dagster, or similar), ingestion and CDC tooling, streaming and batch patterns, data catalogs and lineage, semantic/BI layers, and legacy workflow migration (e.g., Alteryx, SSIS, or similar). Databricks experience is a plus.
- Strong software engineering fundamentals must be an expert in SQL and have solid experience with Python, Git-based workflows, CI/CD, automated testing, and infrastructure-as-code awareness; you hold data engineering to software engineering standards.
- Architecture credibility you have designed platforms, not just built on them: data modeling at enterprise scale, layered architectures, master
data and semantic consistency concerns, and the judgment to know when a pattern is worth standardizing.
- Consulting or professional services delivery background preferred. You have led client-facing engineering delivery under tight timelines, commercial constraints, and contractual milestone commitments, with experience estimating technical work and delivering to defined acceptance criteria (either side of the table: delivery firm or client organization).
- Proven distributed team leadership experience leading onshore/offshore engineering teams, including partner and vendor pods, with quality and accountability consistent across locations.
- AI/ML platform literacy you must understand what ML and GenAI workloads demand of a data platform (feature pipelines, training and inference data flows, retrieval foundations, monitoring) well enough to engineer for them and to lead teams that do.
- Strong communication skills you can defend an architecture to engineers, explain a trade-off to executives, and write design documentation people actually use.
Additional Preferred Skills:
- Experience in financial services in lending, banking, fintech, or another regulated environment including working under data governance, model risk, and audit expectations.
- SnowPro certifications (Core, Advanced Architect, or Data Engineer), dbt certification, or cloud (AWS/Azure) certifications valued as evidence of rigor, not as a substitute for demonstrated delivery experience.
- Experience with data product operating models (data mesh / data product thinking, data contracts, certified metrics) and with building platforms that serve them.
- FinOps experience measuring and managing cloud data platform cost as an engineering discipline.
- Experience standing up engineering practices from scratch on a program: standards, CI/CD, review culture, validation streams, and operational readiness.
Kapitus Total Rewards Package Includes:
- Competitive Base Salary Range of $157,100-$252,000 Kapitus is providing this as a good faith salary range to comply with applicable law. The applicant's final salary will depend on a number of factors including the applicant's geographic location, skills, and experience.
- Annual Incentive Compensation Eligibility – Up to 15% annually
- Health Insurance: Comprehensive medical, dental, and employer-paid vision plans through UnitedHealthcare (UHC), with various coverage levels available to meet the needs of our employees and their families. Additional perks through UHC include: Sweat Equity, free subscription to the Calm App, UHC rewards, Real Appeal, and Quit For Life.
- Flexible Spending Account: Set aside pre-tax dollars from your paycheck to pay for qualified out-of-pocket medical, dental, vision, pharmacy or dependent care expenses.
- Lifestyle Spending Account: Employer sponsored post-tax benefits that allow reimbursement for expenses related to physical, mental and financial well-being.
- 100% Company Paid Insurances: Kapitus fully covers the cost of basic short-term and long-term disability insurance, as well as vision insurance, ensuring our employees have comprehensive protection without any personal expense.
- Voluntary Insurance: Supplemental life insurance as well as enhanced short- and long-term disability coverage are available through Mutual of Omaha, providing additional security for our employees. Additionally, Colonial Accident and Hospitalization insurances are also available, offering further protection against unforeseen events.
- Paid Maternity and Parental Leave: Beyond state-mandated leave policies, Kapitus provides company-paid maternity and parental leave, supporting our employees during important family milestones.
- Commuter Benefits: We offer pre-tax benefits on parking and commuter expenses to cover travel to and from work.
- LifeBalance Program: Enhance your lifestyle with our LifeBalance membership, which offers discounts on outdoor activities, the arts, health, and fitness. Additional benefits include: + Pet and car insurance discounts. + Financial services such as LegalShield. + Relaxation and stress management tools.
- Plum Benefits Discount Program: Access exclusive discounts on shows, travel, car rentals, and more, enriching your personal and family life.
- Tuition Reimbursement: Pursue further education with up to $5,000 annually in tuition reimbursement, plus opportunities to attend relevant conferences and career development events. Managed through our LSA plan, Kapitus Academy.
- Travel Reimbursement: We also offer travel reimbursement for all work-related travel, supporting your involvement in career and personal development activities.
- Paid Time Off and Sick Time.
- Retirement Benefits: Our 401K plan is managed through Fidelity. To support your long-term financial goals, the company provides a 25% match on your contributions, up to 6% of your annual salary.
About Kapitus:
Kapitus is one of the most reliable and respected names in small business financing. As both a direct lender and a marketplace built with a trusted network of lending partners, we can provide small businesses with the financing they need when, and how it is needed. We have spent our entire existence building a culture that makes us excited to come to work in the morning. Our company is fast paced, teammates need to be self-directed and have an internal motivation to do the right thing, even when the right thing takes a lot of hard work. We show our teammates our appreciation by offering great benefits, competitive pay and solid opportunity for growth.
Company Mission: At Kapitus, our mission is to help small business owners grow their organizations by providing tailored, transparent, and ethical financing solutions. We invest in every business owner's story and we are dedicated to building lasting relationships to champion their goals. We promise to keep the best interests of our clients at the center of the financing process by operating with transparency, fairness, and integrity.
Consideration will be given to qualified remote candidates residing in states where Kapitus and/or one of its subsidiaries has an established physical presence.
Salary Context
This $157K-$252K range is above the 75th percentile for Data Engineer roles in our dataset (median: $151K across 28 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 9,653 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Kapitus, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $177,825 based on 160 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($204K) sits 15% above the category median. Disclosed range: $157K to $252K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Kapitus AI Hiring
Kapitus has 2 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer. Based in Arlington, VA, US. Compensation range: $189K - $252K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.