Senior Data Engineer - Corporate Data Platform

We're looking for a Senior Data Engineer to help build and run WatchGuard's corporate data platform: the foundation that stakeholders across the company and our leadership use to run the business. You'll own the design, build, and reliability of our cloud data lakehouse, from raw ingestion through analytics-ready Gold tables. It brings together data from NetSuite, Salesforce, BambooHR, Marketo, and many other systems across the company. This is a deeply technical role, and you'll be expected to think in the architecture of a platform, not just pipeline activities. It's also a business-facing one. You'll work directly with stakeholders across the company to understand their data source and why it's important to the business. You'll also partner closely with our analytics team, who own reporting and analysis on top of the platform you build. What You'll Do Data Platform & Pipeline Engineering Design, build, and maintain scalable pipelines using Azure Data Factory and Apache Airflow, moving data from enterprise business systems through the Medallion architecture (Bronze → Silver → Gold), with incremental loads, CDC, and event-driven ingestion where freshness calls for it. Own data models across every layer, including the Gold layer the analytics team and business users depend on. Build modular, tested dbt models with consistent naming conventions, documentation, and lineage. Build and optimize Snowflake objects for performance and cost. Business Partnership Work directly with business stakeholders to understand what data they need, the business case behind it, and how it will be used, before designing anything. Scope and prioritize new data source requests: what's needed, how fresh, how much history, sensitivity and access, and the effort involved. Partner with the analytics team on the data models, definitions, and data contracts they build on. Data Quality & Observability Build automated validation into every Medallion layer, including freshness checks, schema drift alerts, and anomaly detection, so issues are caught before they reach consumers. Define and enforce data contracts between producers and consumers: row counts, null rate thresholds, referential integrity, and value domains. Build data quality dashboards that give engineering and business stakeholders real-time confidence in platform health and data freshness. Azure Cloud & Infrastructure Manage and optimize Azure Data Lake Storage Gen2: zone and folder structure, lifecycle policies, access tiers, and partitioning. Build and maintain Azure Functions and Logic Apps for event-driven processing, orchestration triggers, and operational automation. Manage secrets and environment configuration securely with Azure Key Vault and managed identities, with no hardcoded credentials in pipelines or code. Help lead our move to infrastructure-as-code for our data platform, provisioning services with Terraform. Engineering Practices & Mentorship Lead code reviews, set and enforce standards, and mentor junior engineers on data engineering best practices. Build out CI/CD for deployments across Dev, Test, and Prod environments. What We're Looking For Must-Have Experience building data platforms that serve business functions such as Finance, Sales, Marketing, or HR, including ingesting data from enterprise applications like NetSuite, Salesforce, BambooHR, and Marketo. Experience working directly with non-technical stakeholders to scope and prioritize data requirements. Snowflake Advanced SQL: window functions, CTEs, and query profiling Native features such as streams, tasks, Snowpipe, dynamic tables, and row-level security Virtual warehouse tuning and credit cost optimization dbt Writing, testing, and documenting production models Incremental strategies and snapshots Data observability: freshness checks, anomaly detection, and schema drift alerting (Elementary or similar) Data Modeling & Medallion Architecture Dimensional modeling and hands-on Medallion architecture implementation Data validation at every layer, not just Gold Schema evolution and SCD Type 2 dimensions Azure Azure Data Factory: pipeline authoring, triggers, parameterization, and linked services ADLS Gen2: zone design, lifecycle management, and Parquet/Delta partitioning Azure Key Vault and managed identities Azure Functions and Logic Apps for event-driven automation Airflow DAG authoring, task dependencies, sensors, and connection management Deployment and monitoring in cloud-hosted environments Python Pipeline scripting and REST API integration Unit testing pipeline logic and transformations 4+ years of professional data engineering experience, including at least 2 years on Azure cloud data platforms. Nice-to-Have Hands-on experience applying AI within data pipelines and workflows, such as LLM-based data enrichment, classification, or anomaly detection (e.g., Snowflake Cortex or similar). Terraform experience provisioning Azure infrastructure. Familiarity with Microsoft Fabric and OneLake as a complementary or future-state platform. Data mesh or data product thinking, and how it maps to ownership across Medallion layers.