Database Engineer

Batchelor & Kimball•Conyers, GA

1d•Onsite

About The Position

Design, build, and establish a greenfield Azure-based data lakehouse platform supporting business analytics, reporting, and AI/ML initiatives. Develop scalable data pipelines, create governed and AI-ready datasets, and partner with business and technical teams to deliver reliable insights and intelligent data solutions. The role includes building platform architecture and standards, supporting business and AI data products, and maintaining the reliability, security, and efficiency of the enterprise data environment.

Requirements

Bachelor’s degree in Data Science/Analytics, Computer Science, Computer Engineering, Information Systems, an associated discipline, or equivalent technical experience.
At least 3 years of hands-on data engineering, database engineering, analytics engineering, or cloud data platform implementation experience.
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.
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.
Familiarity with data platform DevOps practices, including version control, CI/CD, infrastructure as code, monitoring, alerting, performance tuning, cost management, and production support.
Strong communication skills and ability to work directly with business stakeholders, analysts, AI developers, and technical teams.

Nice To Haves

Greenfield data platform implementation experience is strongly preferred.
Experience in smaller teams or broad-ownership roles encompassing architecture, implementation, stakeholder engagement, governance, and ongoing support is preferred.

Responsibilities

Design, build, and implement a new Azure-based data lakehouse platform for business reporting, analytics, and AI/ML use cases.
Develop and maintain scalable ETL/ELT processes to ingest, transform, cleanse, enrich, and deliver structured, semi-structured, and unstructured data.
Implement lakehouse architecture patterns, including bronze/silver/gold layers, Delta Lake table design, and curated data products.
Design and optimize data, semantic, and dimensional models and curated datasets for Power BI reporting, KPI tracking, business analytics, and AI training workflows.
Implement and manage data storage solutions, including Microsoft Fabric OneLake, Azure Data Lake Storage, relational databases, lakehouses, warehouses, and other Azure data platform services.
Ensure data quality, integrity, lineage, metadata management, security, privacy, retention, and governance across platform components.
Collaborate with business stakeholders and operational teams to understand data requirements, validate business logic, and present data meaningfully.
Partner with the AI development team to prepare AI-ready datasets, improve data relevance, support feature engineering, and ensure training data is accurate, traceable, and appropriately governed.
Configure data guardrails, access controls, classification, and filtering rules so AI systems use relevant, approved, and secure data.
Monitor, troubleshoot, and improve the performance, reliability, and cost efficiency of data pipelines, workflows, compute resources, and cloud data services.
Automate data processes using engineering best practices, CI/CD, version control, reusable pipeline patterns, documentation, and operational runbooks.
Partner with business leaders and technical teams to establish data platform standards, architecture decisions, and long-term support practices.
Translate business needs into scalable data solutions in collaboration with stakeholders, analysts, AI developers, and technical teams.