Data Architect

The core responsibilities for the job include the following:

Data Architecture and Modeling:

• Design and maintain enterprise-wide data models, including conceptual, logical, and physical layers.
• Lead data architecture for the Onebot platform and support cross-value-stream data requirements.
• Define data standards, naming conventions, and modeling best practices.
• Evaluate and recommend appropriate data storage patterns (relational, dimensional, lakehouse, graph).

Data Integration:

• Architect and oversee data pipelines and ETL/ELT processes across source systems.
• Design integration patterns for batch, near-real-time, and streaming data flows.
• Ensure robust data lineage, traceability, and auditability across all pipelines.
• Collaborate with engineering teams to implement scalable, resilient integration solutions.

Analytics Enablement:

• Develop semantic and analytical data models that serve BI, reporting, and advanced analytics use cases.
• Partner with analytics and data science teams to define trusted data products.
• Champion a self-service data culture by enabling reusable, well-documented data assets.

Governance and Quality:

• Define and enforce data quality rules, validation frameworks, and remediation processes.
• Contribute to data governance policies, data cataloguing, and metadata management.
• Work with stakeholders to manage data contracts and SLAs.

Stakeholder Collaboration:

• Act as a trusted data advisor across multiple value streams and business units.
• Facilitate technical workshops and design reviews with engineering and product stakeholders.
• Communicate complex data architecture decisions clearly to both technical and non-technical audiences.

Requirements:

• 5+ years of experience in data architecture, data engineering, or a closely related role.
• Strong proficiency in data modeling techniques: relational, dimensional (Kimball/Inmon), and Data Vault.
• Hands-on experience with data integration tools and frameworks (e. g., Apache Kafka, dbt, Spark, Talend, or similar).
• Solid understanding of cloud data platforms (AWS, GCP, or Azure) and modern data stack components.
• Experience with SQL at an advanced level and familiarity with Python or Scala for data engineering tasks.
• Proven ability to translate business requirements into scalable data architecture solutions.

Preferred:

• Prior exposure to conversational AI platforms or event-driven architectures.
• Experience working in agile, value-stream-aligned product organizations.
• Familiarity with data mesh principles and decentralized data ownership models.
• Knowledge of data governance frameworks and metadata management tools (e. g., Collibra, Alation, DataHub).
• Experience with analytical tools such as Looker, Tableau, Power BI, or Metabase.