bkimechanical
Database Engineer
- Compensation
- Not disclosed
- Location
- Conyers, Georgia, United States
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Devops
- Posted
Skills
Must have
- 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 stro
- 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.
Full description
Title: Database Engineer
Location: Conyers, Georgia
Overview
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.
Essential Duties & 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.
Qualifications