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.

Experience
8-12 yrs