Database Engineer
On-siteConyers, Georgia, United States
Job Summary
Design and build a greenfield Azure-based data lakehouse platform to support business analytics, reporting, and AI/ML initiatives. Develop scalable ETL/ELT processes for data ingestion, transformation, cleansing, and delivery across structured, semi-structured, and unstructured sources. Implement lakehouse architecture patterns, including bronze/silver/gold layers and Delta Lake table design, while optimizing data models for Power BI reporting and AI training workflows. Ensure data quality, integrity, lineage, and governance across all platform components. Monitor, troubleshoot, and improve pipeline performance, reliability, and cost efficiency using CI/CD methods and operational runbooks. Partner with business stakeholders and AI development teams to establish data platform standards and deliver intelligent data solutions.
Required Qualifications
- Bachelor's degree in Data Science/Analytics, Computer Science, Computer Engineering, Information Systems, associated discipline, or equivalent technical experience
- Minimum of 3 plus years of hands-on data engineering, database engineering, analytics engineering, or cloud data platform implementation experience
- Demonstrated experience designing, implementing, or significantly contributing to a new data platform, lakehouse, data warehouse, or analytics environment
- Strong working knowledge of Microsoft Fabric components including OneLake, Data Factory, Data Engineering, Data Warehousing, Data Science, Real-Time Analytics, and Power BI
- Experience with Azure data services such as Azure Data Lake Storage, Azure Data Factory, Azure SQL, Synapse, Azure Databricks, Key Vault, RBAC, and related Azure security and governance capabilities
- Hands-on experience with Apache Spark, PySpark, SQL, Python, Delta Lake, lakehouse architecture, data warehousing concepts, and structured/unstructured data processing
- Ability to design reliable ingestion patterns from business systems, APIs, databases, flat files, and cloud sources, including incremental loads, change data capture, validation, and error handling
- Experience preparing clean, governed, documented, and repeatable datasets for AI/ML training, analytics, forecasting, decision support, or advanced automation use cases
- Strong understanding of data governance, security, access controls, privacy, lineage, metadata, data quality, auditability, and responsible AI data boundaries
- Experience building curated datasets, semantic models, dimensional models, star schemas, KPI definitions, and Power BI-ready data products for business users
- Familiarity with DevOps practices for data platforms, including version control, CI/CD, infrastructure as code, monitoring, alerting, performance tuning, cost management, and production support
- Strong communication skills with the ability to work directly with business stakeholders, analysts, AI developers, and technical teams to translate business needs into scalable data solutions
Desired Qualifications
- greenfield implementation experience
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