Enterprise Data Architect

Data Architect (Level 2) Job Description Core Responsibilities (Primary Architecture & Engineering Work) Extensive experience designing, evaluating, implementing, and optimizing Data and Analytics architecture and technologies. Added benefit: hands-on experience with Azure-based Lakehouse components (ADLS, ADF, Databricks, Snowflake, Synapse, MS Fabric). General understanding of networking, server, identity, and security architectures as they relate to data architecture (e.g., how data platforms interact with networks, compute, and security patterns). Define and validate appropriate data access and protection controls across ingestion, storage, transformation, reporting, and data export layers in collaboration with security and platform teams. Define conceptual, logical, and physical data models, including reporting data models, shared measures, consistent business definitions, historical reporting requirements, and authorized data consumption patterns in collaboration with reporting owners. Translate business requirements into appropriate data models, metadata specifications, and data quality requirements. Create and maintain enterprise data and analytics architecture diagrams and roadmaps. Map current-state and target-state data flows across systems and applications. Define and maintain end-to-end data lineage across ingestion, transformation, and consumption layers. Maintain source inventory, ownership, dependencies, and transition plans. Architect, document, and oversee ETL/ELT pipelines and multi-stage transformations. Implement automated CI/CD pipelines to ensure reliable and consistent delivery of data platform components. Design ingestion, staging, curation, and serving layers within Lakehouse environments. Ensure pipeline architectures support reliability, auditability, lineage tracking, and monitoring. Establish and enforce enterprise data standards, metadata definitions, and naming conventions. Define integration patterns for batch, streaming, event-driven, and API-based data flows. Define and maintain source-to-consumer data contracts covering schemas, keys, data freshness, quality expectations, governance standards, compatibility, and change notification. Recommend platform choices for storage, processing, modeling, and analytics, including evaluation of database technologies, storage optimization, and emerging data platform capabilities. Identify opportunities to simplify existing data architecture, promote reusable data services and integration patterns, and optimize platform costs while maintaining scalability and performance. Secondary Responsibilities (Governance, Quality, and Compliance) Align data architecture with enterprise strategy, business architecture, and IT standards. Participate in governance activities (metadata, lineage, stewardship, quality). Conduct peer reviews and quality assurance of data architecture designs and technical artifacts, ensuring alignment with enterprise architecture standards, governance processes, and long-term strategy. Implement data quality validation, profiling, and automated checks within pipelines. Support policies for data security, privacy, retention, and compliance. Ensure consistent enterprise-wide data definitions and semantics. Supporting Responsibilities (Collaboration, Communication & Leadership) Collaborate with data engineers, analysts, DBAs, solution architects in other IT domains, and business teams, including engagement with business stakeholders to align architecture decisions with organizational priorities and strategic roadmaps. Provide guidance and mentorship on architecture patterns and data platform best practices. Contribute to enterprise architecture planning and data modernization roadmaps. Communicate technical concepts clearly to both technical and non-technical audiences. Participate in cross-functional discussions involving data, application, networking, security, systems, database, and governance teams.

• Only those lawfully authorized to work in the designated country associated with the position will be considered.

• Please note that all Position start dates and duration are estimates and may be reduced or lengthened based upon a client’s business needs and requirements.

Enterprise data architecture, data modeling, data warehousing, Lakehouse solution. (5+ yrs), Experience designing and implementing conceptual, logical, and physical data models (5 yrs), Experience translating business requirements into scalable data architecture solutions (5 yrs), Experience designing data integration solutions and working with enterprise databases (5 yrs), Strong proficiency in SQL and understanding of relational database concepts, data modeling (5 yrs), Strong understanding of modern data architectures, including data warehouses, Lake houses (5 yrs), Knowledge of ETL/ELT processes, data pipelines, metadata management, data lineage (5 yrs)

Required Minimum Education: High School Diploma or equivalent

Preferred Education: Bachelor’s Degree

C2C is not available, Relevant education, professional training, or equivalent practical experience in Computer Science, Information Systems, Data Engineering, or a related technical discipline., Minimum of five years of relevant experience in enterprise data architecture, data modeling, data warehousing, Lakehouse solutions, or modern data platform design and implementation. Experience designing and implementing conceptual, logical, and physical data models in enterprise environments. Experience translating business requirements into scalable data architecture solutions. Experience designing data integration solutions and working with enterprise databases, data warehouses, or modern cloud-based data platforms.

Technical Skills & Competencies Strong proficiency in SQL and understanding of relational database concepts, data modeling, and query optimization. Strong understanding of modern data architectures, including data warehouses, Lake houses, cloud data platforms, and data integration patterns. Ability to evaluate database technologies, storage solutions, and data processing platforms based on business and technical requirements. Knowledge of ETL/ELT processes, data pipelines, metadata management, data lineage, and data quality techniques. Understanding of cloud architecture principles and technologies as they relate to enterprise data platforms. Strong understanding of entity-relationship modeling, metadata management, and data quality standards. Ability to assess traditional and modern data architecture components and align architectural decisions with business needs and organizational strategies. Strong analytical, troubleshooting, and problem-solving skills. Strong communication and interpersonal skills, including the ability to present complex architectural concepts to technical teams, business stakeholders, and leadership. Ability to collaborate effectively across cross-functional teams and provide technical guidance on solution design and architecture.