Data Architect - Python - Datalake

Job Summary
The Enterprise Data Architect will lead the design, governance, and evolution of the organization’s enterprise data architecture, establishing the foundational standards, platforms, and patterns that enable scalable analytics, reliable data products, and data-driven decision-making across operations, finance, sales, and M&A activities. This is a strategic and hands-on role responsible for defining how data flows across the enterprise — from source systems through integration layers to consumption-ready platforms — in a fast-paced, private equity-backed environment. The ideal candidate combines deep technical expertise with the ability to translate business objectives into pragmatic, future-ready data architecture decisions. They will serve as a trusted partner to business and technology leaders while guiding a team of data engineers and analysts toward a coherent, governed, and high-performing data ecosystem.

Key Responsibilities

• Enterprise Data Architecture: Define and own the end-to-end enterprise data architecture, including data integration patterns, data modeling standards (conceptual, logical, and physical), cloud platform architecture, and data flow design across ERP, CRM, field service, financial systems, and Business Intelligence (BI). Establish architectural guardrails and reference architectures that guide data engineering and analytics development.
• Data Platform Strategy: Lead the design and evolution of the company’s cloud data platform, including data lake, data warehouse, and lakehouse patterns. Evaluate, select, and govern core platform technologies (e.g., Azure, AWS, Snowflake, Databricks, dbt, Fivetran) to ensure they support current needs and future scale across organic growth and M&A expansion.
• Data Governance and Standards: Establish and enforce enterprise data governance frameworks, including data ownership, stewardship, lineage, metadata management, naming conventions, and data quality standards. Partner with business and IT stakeholders to operationalize master data management (MDM) and implement data catalog tooling (e.g., Collibra, Alation, Purview).
• M&A Architecture and Integration: Serve as the lead data architect for acquisition integration efforts, assessing acquired company data environments, designing integration strategies, and defining standards for onboarding new source systems, historical data migration, and harmonization of data models to support post-acquisition reporting and value tracking.
• Cross-Functional Collaboration: Partner with engineering, analytics, IT, finance, and operations leadership to translate business objectives into architectural decisions. Serve as a bridge between business stakeholders and technical delivery teams, ensuring the data architecture supports both strategic initiatives and day-to-day operational needs.
• Data Modeling and Semantic Layer: Define enterprise-wide data models and semantic layer standards that enable consistent, reusable, and trusted data assets. Ensure analytics-ready datasets are well-structured, documented, and aligned to business definitions, supporting self-service analytics and BI development in tools such as Power BI.
• Performance and Scalability: Design for performance, reliability, and cost efficiency across data ingestion, transformation, storage, and serving layers. Proactively identify architectural bottlenecks and lead remediation efforts to ensure the data platform scales with business growth.
• Technical Leadership and Mentorship: Provide architectural guidance and technical mentorship to data engineers and analytics engineers. Conduct design reviews, establish coding and modeling standards, and foster a culture of engineering excellence and continuous improvement within the data team.
• Continuous Improvement: Stay current with emerging data technologies, architectural patterns, and industry best practices. Identify and champion opportunities to modernize the data estate, improve data reliability, reduce technical debt, and increase the organization’s overall data maturity.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering, or a related field
• 8+ years of experience in data architecture, data engineering, or a related technical discipline, with at least 3 years in an architectural or senior technical leadership role
• Deep expertise in cloud data platform architecture, including data warehouse and lakehouse patterns (e.g., Snowflake, Databricks, Azure Synapse, BigQuery)
• Real-world experience evaluating, explaining, and implementing data fabric and data mesh integration concepts and designs
• Strong proficiency in data modeling (conceptual, logical, physical) and semantic layer design across OLTP and OLAP environments
• Hands-on experience with modern ELT and data transformation tooling including Fivetran, dbt, and cloud-native integration services
• Proven track record designing enterprise-grade data governance frameworks, including metadata management, data lineage, and master data management (MDM)
• Strong SQL skills and proficiency working with relational, columnar, and cloud-native data stores
• Experience architecting data solutions that span multiple heterogeneous source systems, including ERP, CRM, field service management, and financial systems
• Experience architecting data solutions for Business Intelligence reporting with common tools such as Power BI, Tableau, etc.
• Demonstrated ability to communicate complex architectural concepts clearly to both technical teams and senior business stakeholders
• Comfort operating in fast-moving, results-oriented, private equity environments where speed, pragmatism, and business impact matter
• Experience architecting data solutions to support (Gen)AI and Machine Learning capabilities and solutions

Preferred Qualifications

• Experience supporting M&A data integration, including source system assessment, data harmonization, and post-acquisition reporting architecture
• Familiarity with data catalog and metadata management platforms such as Collibra, Alation, or Microsoft Purview
• Exposure to field service, fire and security, or multi-location services industry data environments
• Experience with streaming and real-time data architecture patterns (e.g., Kafka, Event Hubs, Spark Streaming)
• Working knowledge of Python or similar languages for data processing, pipeline automation, and architectural prototyping
• Experience defining and enforcing data architecture standards within a data mesh or data product framework
• Adoption of (Gen)AI/LLMs to accelerate and improve the quality of data architecture deliverables, outputs, and ways of working
• Familiarity with BI tools such as Power BI from an architectural and semantic layer design perspective
• Experience working in a PE-backed, high-growth, or acquisition-intensive business environment