Database Engineer

Batchelor & Kimball•Conyers, GA

18h

About The Position

Design and establish a greenfield, Azure-based data lakehouse platform that supports business analytics, reporting, and AI/ML initiatives. The role develops scalable data pipelines, creates governed and AI-ready datasets, and partners with business and technical teams to deliver reliable insights and data solutions. The Database Analyst builds and maintains ingestion and transformation processes across structured, semi-structured, and unstructured sources; implements lakehouse architecture and curated data products; and designs models and datasets for Power BI, business analytics, and AI workflows. The role also manages Azure data storage and platform services, safeguards data quality and governance, and helps prepare accurate, traceable, and appropriately governed datasets for AI development. The position monitors and improves pipeline performance, reliability, and cost efficiency, and applies automation and data engineering practices such as CI/CD, version control, documentation, and operational runbooks. It partners with business leaders and technical teams to set platform standards, make architecture decisions, and support the enterprise data environment. Batchelor & Kimball describes its workplace as a team environment with high expectations, challenging work, and an emphasis on good working relationships.

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 experience in data engineering, database engineering, analytics engineering, or cloud data platform implementation.
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 and 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 and 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 translate business needs into scalable data solutions while working with stakeholders, analysts, AI developers, and technical teams.

Nice To Haves

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

Responsibilities

Design, build, and implement a new Azure-based data lakehouse platform for reporting, analytics, and AI/ML use cases.
Develop and maintain scalable ETL/ELT processes for ingesting, transforming, cleansing, enriching, and delivering data from structured, semi-structured, and unstructured sources.
Implement lakehouse 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, 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 services.
Ensure data quality, integrity, lineage, metadata management, security, privacy, retention, and governance across platform components.
Work 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 governed.
Configure data guardrails, access controls, classification, and filtering rules so AI solutions reference relevant, approved, and secure data.
Monitor, troubleshoot, and improve performance, reliability, and cost efficiency of 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.