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Data Architect
Job Description
Data Architect
Location: Hybrid Remote in Atlanta (3 days onsite/week). Initial 100% onsite required for the first six months.
Employment Type: Permanent / Direct Hire / Full-time
Salary: Up to $160,000 (depending on experience) + bonus
We're seeking a highly skilled and hands-on Senior Data Architect to lead the design, implementation, and ongoing evolution of our enterprise-grade data systems. This role is crucial for building scalable, secure, and intelligent data infrastructure that supports core analytics, operational excellence, and future AI initiatives. Success requires a seasoned technologist who can seamlessly integrate cloud-native services with traditional data warehousing to create a modern, unified data platform.
Here's what you'll be doing:
Location: Hybrid Remote in Atlanta (3 days onsite/week). Initial 100% onsite required for the first six months.
Employment Type: Permanent / Direct Hire / Full-time
Salary: Up to $160,000 (depending on experience) + bonus
We're seeking a highly skilled and hands-on Senior Data Architect to lead the design, implementation, and ongoing evolution of our enterprise-grade data systems. This role is crucial for building scalable, secure, and intelligent data infrastructure that supports core analytics, operational excellence, and future AI initiatives. Success requires a seasoned technologist who can seamlessly integrate cloud-native services with traditional data warehousing to create a modern, unified data platform.
Here's what you'll be doing:
- Architecture & Strategy: Lead the design and implementation of modern data platforms, including Data Lakes, Data Warehouses, and Lakehouse architectures, to enable a single source of truth for the enterprise.
- Data Modeling & Integration: Architect unified data models that support both modular monoliths and microservices-based platforms. Design and optimize high-volume, low-latency streaming/batch ETL/ELT pipelines.
- Technical Leadership: Drive the technical execution across the entire data lifecycle. Build and optimize core data processing scripts using Spark and Python.
- Governance & Quality: Define and enforce standards for data governance, metadata management, and data observability across distributed systems. Implement automated data lineage tracking, schema evolution, and data quality monitoring.
- Cloud Infrastructure: Configure and manage cloud-native data services, including core data storage and event ingestion infrastructure.
- Experience: 10+ years of proven experience in enterprise data architecture and engineering.
- Core Platform Expertise: Strong, hands-on experience with the Azure Data Ecosystem including Azure Data Lake Storage (ADLS), Azure Synapse Analytics (or equivalent cloud DW), and Azure Purview (or equivalent data catalog).
- Processing: Deep expertise in Databricks (or Apache Spark) for ETL/ELT pipeline implementation, using Delta Lake and SQL Server (or equivalent RDBMS).
- Coding & Scripting: Strong proficiency in Python, Spark, and advanced SQL.
- Data Governance: Hands-on experience implementing data lineage tracking and data quality monitoring (e.g., using Great Expectations or dbt).
- Semantic Technologies: Hands-on experience developing ontology frameworks using OWL, RDF, and SPARQL to enable semantic interoperability.
- Advanced AI Data: Experience integrating structured/unstructured data into Knowledge Graphs and Vector Databases.
- Streaming/Telemetry: Experience developing and maintaining semantic telemetry pipelines using services like Azure Event Hubs or Kafka.
- Emerging Concepts: Exposure to linked data ecosystems, data mesh, or data fabric concepts.
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