Data Architect High Priority

Job Title: Azure Databricks Data Engineer

Job Location: Saint Louis, MO-Onsite

Type of Requirement: Full Time Permanent

Responsibilities

• Significant data engineering experience, including substantial hands-on Databricks development
• experience.
• Azure Databricks - Strong hands-on experience building production-grade batch pipelines and
• lakehouse solutions.
• PySpark / Spark SQL - Advanced transformations, joins, window functions, reusable frameworks,
• debugging, and performance tuning.
• Delta Lake - Table design, schema evolution, merge/upsert patterns, optimization, data reliability,
• and history management.
• Medallion architecture - Practical delivery experience across source-aligned, conformed, and
• consumption-ready layers.
• Data modeling - Fact/dimension design, conformed dimensions, slowly changing dimensions,
• aggregates, and semantic-consumption considerations.
• Data quality and testing - Profiling, rule implementation, reconciliation, unit/integration tests,
• controls, and defect analysis.
• Azure ecosystem - Working knowledge of Azure Data Lake Storage and relevant Azure
• integration, identity, monitoring, and DevOps services.
• DevOps - Git-based development, code reviews, CI/CD, environment promotion, release controls,
• and automated deployment.
• Security/governance - Experience implementing least-privilege access and working with catalog,
• lineage, classification, and row-level security requirements.
• SQL - Advanced SQL development and query optimization for large analytical datasets.
• Hands-on exposure to Databricks Lakeflow Spark Declarative Pipelines or Delta Live Tables and
• the ability to adopt evolving Databricks-native pipeline standards.
• Experience with Unity Catalog; familiarity with enterprise data-governance and data-quality tooling
• such as Informatica.
• Experience with HR data from Workday, Dayforce, PeopleSoft, recruiting, workforce
• management, or talent systems.
• Understanding of Power BI consumption patterns, star schemas, refresh needs, aggregates, and
• performance requirements.
• Experience creating accelerators, reusable engineering templates, metadata-driven pipelines, or
• automated test frameworks.
• Databricks Data Engineer certification or equivalent cloud/data engineering credential.