Software Development Engineer 2- Data Engineering

Own feature-level architecture decisions and author Architecture Decision Records (ADRs)—evaluating alternative technical approaches and documenting recommended paths for technical sign-off.

Manage the dbt "lakefront" project structure, establishing model ownership, group permissions, and approved-team configurations to safely enable team self-service and decentralized model changes.

Contribute to the evolution of team "Golden Paths", service communication standards, and data orchestration workflows.

Translate complex commercial and marketing rules

Perform deep-dive data discovery, technical feasibility assessments, and data volume analysis to quantify business impact and ensure scalability before committing to a build.

Partner actively across engineering, analytics, product, and business stakeholders to reconcile conflicting business logic, align definitions, and drive consensus toward a single source of truth.

Lead feature-level demonstrations and facilitate technical alignment workshops with product managers and cross-functional teams to resolve ambiguity.

Mentor Level 1 engineers through pair programming, structured knowledge-sharing sessions, and constructive, empathetic code/configuration reviews.

Take ownership of feature-level data quality, designing automated regression tests and verification checks to balance risk versus test coverage.

Implement, debug, and leverage AI-augmented engineering workflows (e.g., GitHub Copilot, basic LLM integrations, or automated prompt configurations) to optimize pipeline efficiency and code readability while verifying all outputs against strict enterprise standards.

Qualifications & Requirements

Experience: 3-5 years of professional experience in data engineering, data warehousing, or a software development engineering role focusing on data.

Snowflake Proficiency: Proven track record of independently designing and building highly scalable data models and staging environments inside Snowflake.

dbt (Data Build Tool): Strong hands-on experience managing dbt projects, testing, documentation, and source-control-driven data pipelines.

Preferred Experience: Direct hands-on experience or familiarity with Salesforce Data360 / DataCloud (CDP) ingestion, mapping, and orchestration patterns.

CI/CD & DevOps: Proficient experience utilizing CI/CD automation tools, managing branches, and executing automated code/data quality gates.

Problem-Solving: Strong analytical capability to break down abstract business constraints into concrete data exclusions, filtering matrixes, and performance-tuned queries.