Database Engineer

Batchelor & Kimball•Conyers, GA

8d•Onsite

About The Position

Design, build and establish a greenfield Azure-based data lakehouse platform to support business analytics, reporting, and AI/ML initiatives. This role will 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.

Requirements

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; greenfield implementation experience is strongly preferred.
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.

Nice To Haves

Preference for applicants who have worked in smaller teams or broad-ownership roles where they were responsible for architecture, implementation, stakeholder engagement, governance, and ongoing support rather than only maintaining an existing system.

Responsibilities

Design, build, and implement a new Azure-based data lakehouse platform to support business reporting, analytics, and AI/ML use cases.
Develop and maintain scalable ETL/ELT processes for data ingestion, transformation, cleansing, enrichment, and delivery across structured, semi-structured, and unstructured data sources.
Implement lakehouse architecture patterns, including bronze/silver/gold data layers, Delta Lake table design, and curated data products for business and AI consumption.
Design and optimize data models, semantic models, dimensional models, and curated datasets to support 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 all data platform components.
Collaborate with business stakeholders, and operational teams to understand data requirements, validate business logic, and display data meaningfully.
Collaborate with 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 to improve AI decision making and ensure only relevant, approved, and secure data is referenced.
Monitor, troubleshoot, and improve the performance, reliability, and cost efficiency of data pipelines, workflows, compute resources, and cloud data services.
Automate data processes using data engineering best practices, CI/CD methods, 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 for a newly implemented enterprise data environment.