Coder
At Cadence OneFive, everyone who writes code is called a Coder. This unified title reflects our horizontal culture where impact matters more than hierarchy. Within this role, we recognize different levels of mastery and scope that align with our pay tiers.
This page shows how software engineering competencies combine with responsibilities to create distinct career stages. Engineers typically excel in some areas while developing others—the goal is demonstrating overall impact and capability rather than perfection in every dimension.
Coder, Tier F
Section titled “Coder, Tier F”Pay Tier F | aka “Software Engineer I” or “Engineer I”
Minimum qualifications
Section titled “Minimum qualifications”- Understanding of software development lifecycle
- Technical problem-solving skills
- Experience working in technical teams
- 2 years contributing to an in-production codebase
Mastery level
Section titled “Mastery level”Building proficiency across core software engineering competencies and ramping up on Cadence OneFive’s domain, codebase, and AI-assisted development workflows. Takes features from requirements to completion using AI tools while developing the context and judgment needed to work with increasing independence.
Key competencies at this level
Section titled “Key competencies at this level”- Design and deliver technical solutions - Takes features from requirements to completion using AI tools, seeks guidance on codebase conventions and architectural context
- Collaborate and elevate the team - Asks good questions, documents work clearly, participates in team routines like code reviews and on-call rotations
- Connect technical decisions to product impact - Understands feature requirements and user impact for assigned work, learns building science concepts relevant to work
- Work independently and take ownership - Works independently on defined projects using AI tools, seeks guidance on domain-specific and architectural decisions
- Build reliable software through testing - Implements and tests features using AI-assisted workflows, writes tests following established patterns
- Leverage AI tools effectively - Learns and ramps up on the team’s AI tools and workflows, uses AI tools with guidance, recognizes when AI output needs human verification
Responsibilities
Section titled “Responsibilities”- Implement and test features using AI-assisted development workflows
- Learn core technologies, best practices, and codebase conventions
- Participate in all relevant team routines (code reviews, on-call/meeting rotations)
- Ask questions and document work clearly
- Learn building science/energy efficiency concepts relevant to assigned work
- Ramp up on the team’s AI tools and established development processes
What advancement looks like
Section titled “What advancement looks like”Moving to Tier E requires demonstrating judgment about whether AI is solving the right problem — not just producing working code. This means developing the domain knowledge and codebase familiarity to critically evaluate AI output, suggesting improvements to team processes and AI workflows, and beginning to mentor others.
Coder, Tier E
Section titled “Coder, Tier E”Pay Tier E | aka “Software Engineer II” or “Engineer II”
Mastery level
Section titled “Mastery level”Demonstrates strong judgment in AI-assisted development. Knows when to trust vs. verify AI output, understands what belongs where in the codebase, and recognizes when something feels wrong even if it compiles. The key distinction from Tier F is not execution speed but the ability to interrogate whether AI is solving the right problem.
Key competencies at this level
Section titled “Key competencies at this level”- Design and deliver technical solutions - Contributes to technical decisions within team scope, evaluates whether AI-generated solutions fit the codebase architecture and conventions
- Collaborate and elevate the team - Mentors Tier F engineers on codebase context and AI workflows, leads small technical discussions, improves team processes
- Connect technical decisions to product impact - Suggests feature improvements, understands broader product context and industry workflows
- Work independently and take ownership - Works independently while applying judgment on architectural decisions, knows when to seek guidance on cross-cutting concerns
- Build reliable software through testing - Designs comprehensive test strategies, critically evaluates AI-generated tests for meaningful coverage
- Leverage AI tools effectively - Effective AI pairing with strong trust-vs-verify judgment, improves AI-assisted workflows (with help as needed), comfortably dives into code directly when AI steers off-path or isn’t reaching the root cause, evaluates AI output critically using domain and codebase knowledge
Responsibilities
Section titled “Responsibilities”- Implement features end-to-end, applying judgment on design and trade-offs
- Apply judgment to evaluate whether AI output solves the right problem, not just a problem
- Mentor Tier F coders on domain context, codebase conventions, and AI workflows
- Lead small technical discussions
- Improve team processes and AI-assisted workflows
- Apply industry knowledge to inform technical choices and feature design
- Teach concepts to others
What advancement looks like
Section titled “What advancement looks like”Moving to Tier D requires owning complete project delivery, designing significant systems that others build upon, writing specs for agentic spec-driven development (SDD), and setting technical standards for the team.
Coder, Tier D
Section titled “Coder, Tier D”Pay Tier D | aka “Senior Software Engineer” or “Senior Engineer”
Mastery level
Section titled “Mastery level”Advanced mastery of software engineering competencies. Owns complete project delivery and makes architectural decisions with minimal oversight.
Key competencies at this level
Section titled “Key competencies at this level”- Design and deliver technical solutions - Designs significant systems that other engineers build upon, sets technical standards for the team, partners with product on spec-driven development (SDD) by defining the technical approach for scoped problem statements
- Collaborate and elevate the team - Mentors multiple engineers, leads cross-component initiatives, drives engineering culture
- Connect technical decisions to product impact - Influences product direction, balances technical debt vs. feature work, leverages industry knowledge to guide strategy
- Work independently and take ownership - Owns complete project delivery, makes architectural decisions with minimal oversight
- Build reliable software through testing - Establishes testing standards and quality practices across the team
- Leverage AI tools effectively - Recognizes when AI is confidently wrong, exercises architectural judgment that AI cannot replicate, knows what not to build, designs AI-assisted workflows for the team and helps teammates adopt them
Responsibilities
Section titled “Responsibilities”- Design and deliver complete systems/features that other engineers build upon
- Partner with product on spec-driven development (SDD) — align on the problem and user needs that product defines, then own the technical approach
- Mentor multiple engineers across experience levels
- Lead complex technical initiatives that span components
- Drive engineering culture and best practices
- Make architectural decisions with minimal oversight
- Balance technical debt against feature work
- Think long-term about technical strategy
- Identify incorrect AI-generated solutions that appear correct, and guide the team accordingly
What advancement looks like
Section titled “What advancement looks like”Moving to Tier C requires driving technical strategy across multiple products, solving ambiguous high-impact problems that span the organization, and making architectural decisions that significantly impact company direction.
Coder, Tier C
Section titled “Coder, Tier C”Pay Tier C | aka “Lead Engineer”, “Staff Engineer”, or “Principal Engineer”
Mastery level
Section titled “Mastery level”Expert-level mastery of software engineering competencies. Drives technical strategy across multiple products and owns technical outcomes that directly impact business strategy.
Key distinction
Section titled “Key distinction”While Tier D coders own complete projects within their product, Tier C coders own technical outcomes that span multiple products and directly impact business strategy.
Key competencies at this level
Section titled “Key competencies at this level”- Design and deliver technical solutions - Drives technical strategy across multiple products, makes architectural decisions that significantly impact company direction
- Collaborate and elevate the team - Develops engineering talent organization-wide, serves as advice process stakeholder across multiple products
- Connect technical decisions to product impact - Shapes product strategy through technical lens, integrates industry expertise with technical vision to drive competitive advantage
- Work independently and take ownership - Identifies and solves ambiguous, high-impact problems independently, owns technical outcomes spanning multiple products
- Master the technical stack - Drives technology strategy across the organization, evaluates and adopts new technologies
- Leverage AI tools effectively - Evaluates and drives AI tooling strategy across the organization, identifies bottlenecks and risks across the software development lifecycle, researches and implements solutions, drives adoption of AI-assisted development practices company-wide
Responsibilities
Section titled “Responsibilities”- Identify and solve complex technical problems that span multiple products/systems
- Drive technical strategy across multiple products
- Evaluate and adopt new technologies organization-wide
- Develop engineering talent across the entire organization
- Lead cross-product technical initiatives
- Shape product strategy through technical lens
- Drive technical roadmap alignment
- Make technical decisions that significantly impact company direction and competitive advantage
- Integrate industry expertise with technical vision
What this means in practice
Section titled “What this means in practice”Tier C coders work on problems that don’t have obvious solutions, often requiring them to define both the problem and the approach. They identify gaps that others haven’t noticed, connect dots across product boundaries, and create technical leverage that amplifies the entire team’s impact.
Using these titles externally
Section titled “Using these titles externally”While we use “Coder” internally, we understand external contexts (LinkedIn, industry standards, recruiting) require traditional titles. Use the “aka” titles on your resume and LinkedIn:
- Tier F: Software Engineer I or Engineer I
- Tier E: Software Engineer II or Engineer II
- Tier D: Senior Software Engineer or Senior Engineer
- Tier C: Lead Engineer, Staff Engineer, or Principal Engineer
Choose the variant that best fits your target audience and career goals.
Product-Specific Requirements
Section titled “Product-Specific Requirements”Momentum
Section titled “Momentum”Coders working on the Momentum platform use a specific tech stack:
Core Technologies
Section titled “Core Technologies”- Backend: PHP 8.4 with Laravel framework
- Frontend: Livewire, Alpine.js, Tailwind CSS
- Database: PostgreSQL
- Infrastructure: Laravel Sail (Docker), GitHub Actions CI/CD
- Lint & Testing: Rector, Pint, PHPUnit, Pest, Playwright
Momentum-Specific Skills
Section titled “Momentum-Specific Skills”In addition to the core software engineering competencies, Momentum coders develop expertise in:
Laravel Ecosystem
- Eloquent ORM and database migrations
- Livewire for reactive interfaces
- Laravel’s authentication and authorization systems
- Queue management and background jobs
- Event sourcing and domain-driven design patterns
Building Science Domain Knowledge
- Energy and building performance vocabulary and concepts
- Utility incentive programs and regulatory compliance vocabulary and concepts
Quality and Testing
- PHPStan static analysis (level 9 for new code, Level 5 at CI-CD)
- Comprehensive test coverage (unit, feature, browser)
- Test-driven development practices
- Performance testing and optimization
Learning Resources
Section titled “Learning Resources”New Momentum coders should familiarize themselves with:
- Laravel documentation and best practices
- Livewire component patterns
- Building science fundamentals (provided during onboarding)
- The codebase’s architecture documentation
The same tier progression (F/E/D/C) applies to Momentum coders, with mastery demonstrated through both technical execution and growing domain expertise in building decarbonization.
BKB (Building Knowledge Base)
Section titled “BKB (Building Knowledge Base)”BKB is Cadence OneFive’s algorithmically weighted and sanitized data store for building information. Coders working on BKB focus entirely on database architecture and data pipelines using both ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) patterns.
Core Technologies
Section titled “Core Technologies”- Database: PostgreSQL with advanced indexing and query optimization
- Pipeline Framework: Python-based data pipelines supporting ETL and ELT patterns
- Transformation: dbt (data build tool) for SQL-based transformations
- Data Quality: Data validation, deduplication, and sanitization algorithms
- Infrastructure: Docker, Airflow/scheduled jobs, GitHub Actions CI/CD
- Monitoring: Database performance monitoring, data quality metrics
BKB-Specific Skills
Section titled “BKB-Specific Skills”In addition to the core software engineering competencies, BKB coders develop expertise in:
Database Architecture
- PostgreSQL schema design and optimization
- Advanced indexing strategies (B-tree, GiST, partial indexes)
- Query performance tuning and execution plan analysis
- Database constraints and referential integrity
- Partitioning strategies for large datasets
ETL/ELT Pipeline Engineering
- Data extraction from diverse sources (APIs, files, databases)
- ETL: Transform data before loading into the database
- ELT: Load raw data first, transform using SQL and database features
- dbt for SQL-based transformations, testing, and documentation
- Data quality validation and error handling
- Incremental vs. full load strategies
- Pipeline orchestration and scheduling
Data Quality and Sanitization
- Algorithmic weighting of data from multiple sources
- Deduplication and entity resolution
- Data validation rules and constraints
- Data lineage tracking and provenance
- Anomaly detection and correction
Building Data Modeling
- Structured data models for building components and systems
- Relationship modeling between buildings, systems, and performance data
- Temporal data handling (historical changes, versioning)
- Geospatial data for building locations
- Integration with building performance standards and taxonomies
Learning Resources
Section titled “Learning Resources”New BKB coders should familiarize themselves with:
- PostgreSQL advanced features and performance tuning
- dbt documentation and best practices
- ETL/ELT design patterns and best practices
- Building science terminology and data standards
- The BKB data model, schema documentation, and data quality framework
The same tier progression (F/E/D/C) applies to BKB coders, with mastery demonstrated through both technical execution in database/ETL systems and ability to model complex building data relationships.
Calculation Service
Section titled “Calculation Service”Coders working on the Calculation Service use Python for scientific computing:
Core Technologies
Section titled “Core Technologies”- Language: Python 3.11+
- Framework: FastAPI
- Scientific Computing: NumPy, Pandas, SciPy
- Testing: pytest, hypothesis (property-based testing)
- Type Checking: mypy, Pydantic
- Infrastructure: Docker, Kubernetes, GitHub Actions CI/CD
Calculation Service-Specific Skills
Section titled “Calculation Service-Specific Skills”In addition to the core software engineering competencies, Calculation Service coders develop expertise in:
Scientific Python
- NumPy for numerical computations
- Pandas for data manipulation and analysis
- SciPy for scientific algorithms
- Vectorized operations for performance
- Memory-efficient data processing
Energy Modeling Domain
- Building energy simulation algorithms
- HVAC load calculations
- Heat transfer and thermodynamics
- Weather data processing
- Energy code compliance calculations
API Design and Performance
- FastAPI for high-performance APIs
- Pydantic for data validation and serialization
- Asynchronous request handling
- Caching strategies for computation-heavy operations
- Rate limiting and resource management
Quality and Testing
- Property-based testing with hypothesis
- Numerical accuracy and precision handling
- Performance benchmarking
- Integration testing with building models
- Validation against reference implementations
Learning Resources
Section titled “Learning Resources”New Calculation Service coders should familiarize themselves with:
- Python scientific computing stack
- FastAPI and async Python patterns
- Building energy modeling fundamentals (ASHRAE standards)
- The calculation engine architecture and validation suite
The same tier progression (F/E/D/C) applies to Calculation Service coders, with mastery demonstrated through both technical execution and deep understanding of building science and energy modeling principles.