Senior Data Architect (English required)

BETSOL

5d•Onsite

About The Position

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.

Requirements

Bachelor’s degree in Cyber Security, Computer Science or related field (or equivalent experience)
8+ years in data architecture, data engineering, or enterprise data management.
Strong hands-on experience with Snowflake, DBT, Data Vault 2.0, Streamlit / Python
Strong hands-on experience with AWS data engineering and Kafka
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.
Leadership skills in a cross-functional environment.
Experience supporting analytics, AI/ML use cases, and data warehousing at scale.
Strong English proficiency required

Nice To Haves

Strong experience with BI tools; Tableau, Looker, Power BI
AWS Certified Data Analytics – Specialty
TOGAF (for architecture frameworks)
CDMP (Certified Data Management Professional)

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