Associate Data Engineer

Hybrid - 3 days a week in the office after getting up to speed.

Location:

Clayton, MO

• Design, develop, and maintain cloud-based data infrastructure using technologies such as Azure SQL Database, Azure Data Lake, Azure Synapse Analytics, and DuckDB (AWS experience also considered).
• Build scalable data pipelines capable of processing large and complex datasets from multiple enterprise sources.
• Develop and manage cloud data solutions utilizing Azure Synapse Spark Pools, Data Pipelines, Spark Notebooks, and DBT.
• Create optimized data models and implement efficient ETL/ELT processes, transformations, and metadata frameworks.
• Integrate data from transactional systems, on-premise warehouses, enterprise databases, and SaaS platforms into modern cloud data environments.
• Maintain and enhance existing data models and analytics infrastructure.

• Implement data validation, monitoring, error handling, security controls, and performance optimization techniques.
• Ensure reliability, scalability, and resilience across data platforms.
• Analyze datasets to identify anomalies, trends, and meaningful business insights.
• Support initiatives migrating legacy data and BI platforms to cloud-based data lakes and lakehouse architectures.
• Enable advanced analytics and reporting capabilities for business stakeholders.
• Troubleshoot data issues and recommend practical technical solutions.

• 0–2 years of experience in Data Engineering, ETL development, or related roles (internship experience acceptable).
• Strong proficiency in SQL and Python.
• Experience or exposure to cloud data platforms (Azure preferred).
• Understanding of:
• Data warehousing concepts
• ETL/ELT processes
• Structured and unstructured data management
• Bachelor’s degree in Computer Science, Information Systems, Analytics, or related field.

• Experience with Azure Synapse Analytics, Spark environments, or similar cloud analytics platforms.
• Exposure to Power BI or Tableau.
• Familiarity with DAX or Power Query (M).
• Understanding of CI/CD pipelines and Dev Ops practices.
• Experience working with large-scale or distributed datasets.

• Build and scale data infrastructure aligned with organizational needs.
• Support statistical analysis and data classification initiatives.
• Leverage existing data environments to fulfill analytics and reporting requests.
• Monitor data performance and recommend improvements.
• Perform additional duties as required to support client and project success.