About the Role: The Senior Data Architect is responsible for designing and implementing the enterprise data architecture. This role will lead the development of scalable, secure, and integrated data platforms to support analytics, compliance, and business operations. Responsibilities: Define and implement the company’s data architecture, models, and pipelines. Design integrations between internal platforms and third-party systems (Salesforce, CX platforms, etc.). Establish standards for data quality, metadata, lineage, and classification Partner with engineering, security, and compliance teams to align on privacy, retention, and access control policies. Lead the selection and implementation of data platforms. Drive adoption of data best practices across teams and support data governance efforts. Support data risk reviews, vendor onboarding, and audit readiness from a technical standpoint. Maintain data platforms and architecture performance Support existing data workflows, and integrations Respond to data-related incidents and ensure data reliability Improve data models to support business growth and analytics Optimize data pipelines, quality, and availability Expand self-service data capabilities for stakeholders Define future-state architecture to enable digital transformation Lead data strategy to support AI/ML, automation and advanced analytics Integrate data across new platforms, partners, or business models Looking For: 8+ years in data architecture, data engineering, or enterprise data management.  Mandatory Skills: A. Technical Skills:  Demonstrates strong and effective communication with clients and cross-functional stakeholders.   Strong hands-on experience with modern cloud data platforms (Snowflake)   Strong hands-on experience in AWS data engineering services (e.g., S3, Glue, Redshift, Lambda)   Familiarity with privacy and compliance frameworks such as SOC 2, HIPAA, GDPR, PCI DSS.   Strong understanding of data modeling (OLTP/OLAP), APIs, and system integration.  Proven experience owning or leading enterprise data architecture (not just pipeline development or BI)  Experience supporting analytics, AI/ML use cases, and data warehousing at   scale.  Advanced proficiency in Python for data engineering and automation  Experience with data governance concepts (quality, lineage, metadata, classification)  B. Soft Skills Strong communication skills with clients, business stakeholders, and cross-functional teams Ability to collaborate effectively across multiple functions Demonstrated leadership skills in a cross-functional environment Ability to drive and influence enterprise-level data strategy and architecture decisions Good to Have Skills: Experience with Data Vault 2.0 or other enterprise modeling approaches (Kimball, Inmon, 3NF, etc.)  Experience with DBT or similar transformation tools (Coalesce, Madillion, Dataform, or native SQL-based transformations)  Experience with Kafka or equivalent streaming platforms (Kinesis, MSK, Confluent, etc.)  Experience building data applications or self-service tools (Streamlit or similar frameworks)  Strong experience with BI tools; Tableau, Looker, Power BI  AWS Certified Data Analytics – _Specialty  TOGAF (for architecture frameworks)  CDMP (Certified Data Management Professional)