Senior Data Engineer

Responsibilities Design, build, and maintain scalable ETL/ELT pipelines using Azure Data Factory (ADF) and Apache Airflow, processing structured and semi-structured data across the Medallion architecture (Bronze → Silver → Gold). Implement incremental load patterns, change data capture (CDC), and event‑driven ingestion to ensure data freshness across the platform. Build and optimise Snowflake data warehouse objects—including tables, views, dynamic tables, streams, tasks, and stored procedures—for performance and cost efficiency. Develop modular, tested dbt models aligned to each Medallion layer, enforcing consistent naming conventions, documentation, and lineage across all transformations. Embed automated data validation at every Medallion layer using Elementary to ensure anomaly detection, freshness checks, and schema drift alerts are in place before data reaches consumers. Define and enforce data contracts between producers and consumers—row count checks, null rate thresholds, referential integrity, and value domain validation. Build and maintain data quality dashboards to give engineering and business stakeholders real‑time confidence in platform health. Manage and optimise Azure Data Lake Storage Gen2 (ADLS) folder structures, lifecycle policies, access tiers, and partition strategies. Build and maintain Azure Functions and Azure Logic Apps for lightweight event‑driven processing, orchestration triggers, and operational automation. Manage secrets, credentials, and environment‑specific configuration securely using Azure Key Vault—no hardcoded credentials in pipelines or code. Contribute to infrastructure‑as‑code practices (Terraform or Bicep) for provisioning Azure data services. Translate ambiguous business requirements into well‑defined data models and pipeline designs, working with analysts and stakeholders to validate assumptions before build. Participate in code reviews, enforce standards, and mentor junior engineers on data engineering best practices. Support CI/CD adoption for pipeline and dbt model deployment across Dev / Test / Prod environments. Qualifications – Must Haves 4+ years of professional data engineering experience, with at least 2 years on Azure cloud data platforms. Advanced SQL skills—window functions, CTEs, recursive queries, query profiling. Experience with Snowflake, including streams, tasks, snowpipe, dynamic tables, row‑level security, virtual warehouse tuning, and credit cost optimisation. Proficiency with dbt and Elementary: writing, testing, and documenting production dbt models; Elementary integration for data observability and anomaly detection; dbt incremental strategies, snapshots, and semantic layer usage. Strong Azure cloud knowledge: Azure Data Factory pipeline authoring, triggers, parameterisation, linked services; ADLS Gen2 zone/folder design, lifecycle management, Parquet/Delta partitioning; Azure Key Vault secret management and managed identities; Azure Functions/Logic Apps event‑driven triggers and lightweight automation. Airflow experience: DAG authoring, task dependencies, XCom, sensors, connection management, deployment, and monitoring in cloud‑hosted environments. Python skills: data pipeline scripting, basic PySpark, REST API integration, unit testing of pipeline logic and transformation functions. Hands‑on experience implementing Bronze, Silver, Gold Medallion architecture; data validation checks at each layer; schema evolution handling and SCD Type 2 dimension management. Nice‑to‑Have Skills Exposure to Snowflake Cortex, dbt Semantic Layer, or Boomi Data Hub for AI‑assisted data enrichment within pipeline layers. Experience integrating LLM‑based quality checks or AI‑assisted anomaly detection into data workflows. Familiarity with Microsoft Fabric and OneLake as a complementary or future‑state platform. Knowledge of data mesh or data product thinking and how it maps to Medallion layer ownership. Experience with Terraform or Bicep for Azure infrastructure provisioning. Compensation Base salary range: $140,000 – $150,000 per year for full‑time employment, exclusive of benefits. Salary determined by individual skills, education, and experience. U.S. Benefits Comprehensive benefits plan including medical, dental, vision, disability, and life insurance. Healthcare HSA with employer contribution. 10 paid holidays. 10 days of paid annual leave. 9 days of paid sick time. Paid parental leave. 401(k) with employer match. Other Perks Education assistance program. Dependent Care HSA match. Adoption assistance. Fertility care support. Backup care for family and pets. A growing network of employee resource groups. Employee referral program. Employee Assistance Program. Equal Opportunity Employer WatchGuard provides equal employment opportunities for all qualified employees, regardless of race, color, national origin, religion, ancestry, creed, pregnancy, age, sex, sexual orientation (including gender expression or identity), marital status, mental or physical disability, honorably discharged veteran or military status, or any other category protected by federal, state, or local laws. Our equal employment opportunity (EEO) policy focuses solely on talent, hard work, contributions, and actual results achieved by each employee and on the potential of employment candidates to make such contributions. We consider focusing on an employee's protected characteristics rather than talent, hard work, and actual work results to violate our EEO policy. As an Equal Opportunity Employer, we are committed to a diverse workforce. 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