OverviewMcGuireWoods LLP is seeking a Data Architect to join our IT team. The Data Architect is responsible for the design and technical architecture of the Firm's Data Lakehouse environment and ensures that data is appropriately structured, integrated, and available to support the Firm's business intelligence, reporting, analytics, and AI initiatives. The position works closely with the Database Platform Lead, business stakeholders, and technology teams to understand business and reporting requirements and translate those requirements into scalable, secure, and maintainable data solutions.
The position works closely with the Database Platform Lead and downstream developers to ensure Data Lakehouse implementations follow approved architecture, technology standards, security requirements, and established business requirements, and provides technical guidance throughout the design and implementation lifecycle.
Responsibilities- Designs and maintains the technical architecture of the Firm's Data Lakehouse environment, ensuring solutions are scalable, secure, maintainable, and aligned with established technology standards.
- Works closely with the Database Platform Lead and business stakeholders to understand business processes, data requirements, reporting needs, and integration requirements and translates those requirements into technical data solutions.
- Identifies and evaluates data sources required to support business intelligence, reporting, analytics, AI, and other approved data use cases.
- Designs data structures, data flows, integration approaches, and data mappings to ensure data is appropriately organized and available for downstream use.
- Configures and maintains data connectors and integrations between the Data Lakehouse and Firm applications, databases, and other approved data sources.
- Works with the Database Platform Lead and source system owners to understand source data structures, identify data dependencies and limitations, and resolve data integration issues.
- Establishes and applies appropriate data modeling, transformation, integration, performance, security, and data quality practices within the Data Lakehouse environment.
- Works closely with the Business Intelligence Developer, AI Solutions Engineer, Senior Software Developers, and other technology resources to ensure data structures and integrations effectively support downstream solutions.
- Participates in the evaluation, design, testing, implementation, and optimization of Data Lakehouse capabilities and integrations.
- Provides technical guidance regarding data architecture, integration patterns, data structures, and platform capabilities and helps ensure implementations conform to approved architectural standards.
- Documents data architecture, data mappings, data flows, source systems, dependencies, integration designs, and other technical information necessary to support the Data Lakehouse throughout its lifecycle.
- Evaluates emerging data platform, cloud, analytics, automation, and AI technologies and recommends appropriate architectural approaches and improvements to the Firm's Data Lakehouse environment.
- Keeps abreast of emerging data architecture practices, technologies, and industry trends and applies appropriate developments to improve the Firm's ability to leverage enterprise data.
QualificationsRequired:- Bachelor's degree in Computer Science, Information Systems, Data Engineering, or a related field, or equivalent relevant experience.
- Minimum of five years of experience in data architecture, data engineering, data integration, data warehousing, or a related technical discipline.
- Experience with Microsoft Azure and Databricks.
- Demonstrated experience designing and implementing enterprise data solutions, including data structures, data models, data integrations, and data pipelines.
- Strong proficiency in SQL and experience working with relational databases, data warehouses, and enterprise data platforms.
- Strong understanding of data modeling concepts, including relational and dimensional modeling, data structures, relationships, keys, and data dependencies.
- Hands-on experience with ETL/ELT processes, data integration patterns, data transformation, data mapping, and data validation.
- Experience identifying, analyzing, and integrating data from multiple enterprise applications, databases, APIs, or other data sources.
- Experience working with cloud-based data platforms and modern data architecture concepts, including data lake, data warehouse, or Lakehouse architectures.
- Experience designing and implementing secure, scalable, and maintainable data solutions that support business intelligence, reporting, analytics, or other downstream applications.
- Demonstrated ability to translate business and reporting requirements into technical data architecture and integration solutions.
- Understanding of data quality, data security, access control, data lineage, and governance principles as they apply to enterprise data solutions.
- Strong analytical, troubleshooting, problem-solving, documentation, and communication skills, with the ability to explain technical data concepts to both technical and non-technical audiences.
- Ability to work effectively with business stakeholders, application owners, developers, architects, vendors, and other technology resources.
Preferred:- Experience working with a cloud-based data Lakehouse, data warehouse, enterprise analytics, or comparable data platform, including hands-on experience with Entegrata or similar technologies.
- Experience with Microsoft Fabric, Azure Data Factory, Azure Data Lake Storage, or related cloud data technologies.
- Experience with Power BI, Power BI semantic models, or other enterprise business intelligence and reporting platforms.
- Experience with data integration technologies and methods, including APIs, REST/JSON, database connectivity, file-based integrations, and data connectors.
- Experience with Python, PySpark, or other programming languages used for data engineering and automation.
- Experience with data pipeline orchestration, scheduling, incremental loading, error handling, monitoring, and deployment.
- Familiarity with enterprise data architecture, dimensional modeling, data quality, data governance, metadata management, and data lineage concepts.
- Experience integrating data from enterprise applications, particularly financial, time and billing, HR, CRM, document management, and matter management systems commonly used by law firms.
- Experience supporting data platforms, analytics, or data solutions in a law firm, professional services organization, or other confidentiality-sensitive or highly regulated environment.
- Experience with Azure DevOps, Git, CI/CD, or other tools supporting source control and deployment of data solutions.
- Experience supporting analytics, artificial intelligence, machine learning, or other advanced data use cases.
- Relevant Microsoft Azure, Databricks, data engineering, or cloud certifications.
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