Data Engineer - Snowflake & DBT

Kapitus•Arlington, VA

7h•$96,300 - $154,400

About The Position

Kapitus is hiring Data Engineers to build a modern enterprise data and AI platform for its financial services organization. The multi-year modernization program replaces legacy analytics workflows with governed, reusable business data products, establishes an AI/ML platform, and shifts the organization from producing reports to producing decisions. The core technology stack is Snowflake and dbt, organized around layered medallion-style architecture with governed conformed entities, certified semantic definitions, and reusable serving structures. The role builds ingestion and transformation pipelines, models data across platform layers, migrates legacy workflows, and delivers data products through evidence-based acceptance gates. The position reports to the Director of Data Engineering and works with internal engineers and consulting partners across onshore and offshore locations. Delivery is governed by approved designs, timelines, and acceptance criteria; completed work must be validated, supported with evidence, and independently accepted. Senior Data Engineers additionally own complex workstreams or product slices, lead technical design within the approved architecture, review work, mentor colleagues, and drive difficult migrations or production issues to closure. The position may be considered for qualified remote candidates residing in states where Kapitus or a subsidiary has an established physical presence.

Requirements

At least 5 years of hands-on data engineering experience building production data pipelines and warehouse models.
For Senior Data Engineer consideration, at least 8 years of experience and demonstrated workstream-level technical leadership, design ownership, code-review responsibility, and mentoring.
Strong hands-on Snowflake experience, including warehouse and database objects, performance- and cost-aware SQL, role-based access concepts, tasks/streams or equivalent patterns, and production-scale loading and unloading.
Production dbt experience building and maintaining projects, including layered model organization, incremental models, tests, documentation, macros, and collaboration in a shared multi-developer project with CI.
Expert SQL skills, including complex transformations, window functions, and performance tuning, with judgment about when SQL is not the right tool.
Solid Python experience for data engineering ingestion utilities, API integrations, automation, and orchestration code.
Experience with layered or medallion architecture and understanding of layer discipline, grain, and conformed entities.
Engineering fundamentals including Git, code review, CI/CD, environment promotion, and automated testing as normal practice.
A thorough data quality and verification mindset, including testing and reconciling work and producing complete evidence that pipelines are correct.
Clear written communication for design notes, pull request descriptions, and documentation that works across time zones.

Nice To Haves

Experience migrating legacy analytics workloads such as Alteryx, SSIS, SAS, or stored-procedure estates to a modern cloud stack, including parity testing against legacy output.
Experience in financial services, lending, banking, fintech, or another regulated environment, including sensitive data classifications and audit expectations.
Consulting or client-facing delivery experience under tight timelines and contractual acceptance criteria.
Experience with governed architecture variants that include conformed/core entity models, certified metrics, semantic definitions, or data contracts.
Orchestration experience with Airflow, Dagster, or similar tools; CDC or ingestion tooling; and streaming or near-real-time patterns.
Exposure to data catalogs and lineage tooling, semantic or BI layers such as Tableau or Sigma, and ML/AI data needs such as feature pipelines, training datasets, or retrieval foundations.
SnowPro or dbt certifications.

Responsibilities

Develop ELT pipelines in Snowflake and dbt, including source ingestion, layered transformations, and governed serving structures for BI, API, ML, and AI consumption.
Model data across staging, conformed core entities, dimensional models, and product-level models according to approved standards for grain, keys, and naming.
Apply approved data contracts, conformed entity definitions, keys, relationships, and certified semantic definitions consistently across models.
Migrate legacy analytics workflows to dbt and Snowflake, analyze existing logic, demonstrate parity where required, and support retirement of legacy assets.
Implement data quality and observability controls as code, including dbt tests, source freshness checks, reconciliation and validation queries, and pipeline observability.
Contribute reusable ingestion frameworks, transformation patterns, and serving structures rather than one-off solutions.
Use Git-based workflows, peer code review, CI/CD, environment promotion, and operational documentation.
Follow and help improve platform engineering standards, including repository structure, dbt conventions, testing requirements, and performance and cost discipline.
Profile query performance, manage Snowflake compute costs, and improve efficiency.
Support production pipelines by diagnosing failures, participating in incident and root-cause analysis, implementing durable corrective actions, and preventing recurring defects.
Identify risks and ambiguities early and get specifications clarified rather than silently improvising.
Build to approved designs and acceptance criteria, produce developer verification evidence, and support independent acceptance validation.
Collaborate across distributed internal and partner teams through clear written communication, clean handoffs, and deliberate use of overlap hours.
Estimate work honestly, flag schedule or scope variance early, and promptly resolve validation defects with their root causes understood.
For senior-level candidates: own complex workstreams or product slices, lead technical design within the approved architecture, review other engineers’ work, mentor team members, and lead difficult migrations or production issues to closure.

Benefits

Competitive base salary of $96,300–$154,400, depending on factors including location, skills, and experience.
Annual incentive compensation eligibility of up to 10%.
Medical, dental, and employer-paid vision plans, with multiple coverage levels.
UnitedHealthcare perks, including Sweat Equity, a Calm App subscription, UHC rewards, Real Appeal, and Quit For Life.
Flexible Spending Account for qualified medical, dental, vision, pharmacy, or dependent-care expenses.
Lifestyle Spending Account for reimbursement of physical, mental, and financial well-being expenses.
Company-paid basic short-term and long-term disability insurance and vision insurance.
Optional supplemental life insurance and enhanced short- and long-term disability coverage, plus accident and hospitalization insurance.
Company-paid maternity and parental leave beyond state-mandated policies.
Pre-tax commuter benefits for parking and commuting expenses.
LifeBalance membership discounts for outdoor activities, arts, health, and fitness, plus pet and car insurance discounts, LegalShield, and relaxation and stress-management tools.
Plum Benefits discounts on shows, travel, car rentals, and other offerings.
Tuition reimbursement up to $5,000 annually and opportunities to attend relevant conferences and career-development events.
Travel reimbursement for work-related travel.
Paid time off and sick time.
401(k) plan through Fidelity with a 25% match on employee contributions, up to 6% of annual salary.