Software Development Engineer 2 – Data Engineering

WEX•Boston, MA

1d•$96,100 - $115,500

About The Position

Are you a technical artisan who thrives in collaborative environments and gets excited about solving the right problems, the right way? Do you believe in breaking down silos and fostering a culture of shared responsibility? Then this role is for you! In today's software development and data landscape, collaboration, end-to-end (E2E) accountability, problem-solving, and streamlined workflows are key to achieving efficiency and delivering high-quality solutions that solve customer problems and generate business outcomes. We believe in the power of integrated engineering, where development, data quality, architecture, and agility skills blend together throughout the solution delivery pipeline. As a Software Development Engineer 2 (SDE 2) focusing on Data Engineering & Data Architecture, you will be a champion for this approach. You will own the data pipelines and modeling that power our Customer Data Platform (CDP), directly feeding marketing/commercial segmentation and customer journeys. In this role, you will match the expectations of an Intermediate Individual Contributor (EAE 2), acting with independence to own specific modules, functional areas, and data models without constant oversight.

Requirements

3-5 years of professional experience in data engineering, data warehousing, or a software development engineering role focusing on data.
Proven track record of independently designing and building highly scalable data models and staging environments inside Snowflake.
Strong hands-on experience managing dbt projects, testing, documentation, and source-control-driven data pipelines.
Proficient experience utilizing CI/CD automation tools, managing branches, and executing automated code/data quality gates.
Strong analytical capability to break down abstract business constraints into concrete data exclusions, filtering matrixes, and performance-tuned queries.

Nice To Haves

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

Responsibilities

Design and build robust data models in Snowflake using dbt, spanning from staging through to production data marts, ensuring they are resilient, cost-effective, and maintainable.
Integrate, stage, and reconcile data from multiple complex source systems (e.g., Siebel CRM, Enterprise Data Warehouse (EDW) snapshots, portfolio health hubs) into a unified Customer Data Platform.
Add field enhancements and manage the evolution of the CDP Mart to power downstream segmentation and outbound journeys.
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.