Data Engineer

Description

**Role Mission** The Data Engineer role is predominantly technical and execution-oriented. We are seeking someone who enjoys writing code, building robust pipelines, and rapidly addressing the business’s evolving needs. The Data Engineer will be the technical owner of Azure Data Factory (ADF) and dbt—the tools that power Snowflake’s Data Lake—and will serve as the critical enabler allowing the team to move quickly without breaking anything. Their work is measured in tangible deliverables: functioning ETLs, business-reflecting models, and clean, timely data delivered to those who need it. **Key Responsibilities** **Data Transformation and Modeling (dbt)** * Serve as the team’s technical dbt subject-matter expert: model design, Staging / Intermediate / Mart layers, quality testing, and documentation. * Develop and maintain Silver and Gold data lake models in coordination with domain data owners. * Establish and enforce development conventions (naming, lineage, testing, materialization). * Integrate dbt with existing data observability and governance tools. * Contribute to the data platform’s architecture and roadmap. **Data Ingestion and Orchestration (Azure Data Factory)** * Be the technical owner of ADF pipelines: design, deployment, monitoring, and incident resolution. * Manage ingestion from current source systems (ERP, CRM, marketing tools, etc.) into Snowflake’s Bronze/Silver layers. * Optimize performance and cost of existing pipelines, and design new ones as scope expands. **Data Quality, Governance, and Collaboration** * Adopt a *Data Governance first* mindset: naming, lineage, classification, and traceability are integral to the work—not an afterthought. * Work side-by-side with BI analysts, marketing automation specialists, and the AI Taskforce to ensure data arrives on time and in optimal condition. * Act as the technical counterpart to the external platform vendor, safeguarding delivery consistency and quality. * Foster a data quality culture: automated tests, documentation, lineage, and identification of issues for resolution with data stewards. * Maintain an up-to-date data catalog and support the Governance team in evolving data assets. * Maintain and evolve the current security framework. * Apply security policies and data masking rules to ensure privacy and appropriate usage. **Required Technical Profile** **Mandatory** * Proven experience with dbt Core or dbt Cloud in production environments (2\+ years). * Solid knowledge of Azure Data Factory: pipelines, triggers, linked services, Integration Runtimes. * Advanced SQL proficiency and experience with Snowflake or similar cloud platforms (BigQuery, Redshift). * Familiarity with data architecture patterns: Medallion (Bronze/Silver/Gold), Data Vault, dimensional modeling. * Experience with Git and CI/CD workflows for data projects. **Desirable** * Python knowledge for data engineering tasks (PySpark, Pandas, automation scripts). * Experience with data governance platforms or catalogs, or dbt Semantic Layers. * Familiarity with Power BI or modern BI tools as consumers of data layers. * Knowledge of AI/ML tools in the data context (MLflow, Feature Stores, etc.). * Experience with tools such as Ask Your Data. **Personal Profile** * Ownership orientation: capable of end-to-end technical domain ownership, with autonomy, integrity, and a strong sense of responsibility. * Service mindset: understands their work exists to empower others’ better decision-making. Data is the means; people are the end. * Excellence and continuous improvement: proposes standards, documents thoroughly, and never leaves technical debt unmanaged. * Clear communication with non-technical stakeholders: able to translate engineering decisions into business language. * Team collaboration in small, agile environments, with direct exposure to leadership. * Adaptability to a building environment: unafraid of blank pages or making decisions with incomplete information.