Senior Data Architect - CVM & Marketing Analytics

SSC HR Solutions

Senior Technical Data Architect We are looking for a Senior Technical Data Architect to own the technical data architecture end to end, defining how data is ingested, stored, processed, served, and governed across the data platform. The role is responsible for setting the technical standards and architectural direction that data engineering teams build against, with a strong focus on scalability, reliability, performance, data quality, and maintainability.

Posted 9/20/2026full-timeRemote • EgyptSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and implementing scalable data architectures, including lakehouse and OLAP systems, while ensuring data quality, governance, and lifecycle management. Proficient in leading data engineering teams and establishing technical standards for data platforms.

Highest-signal resume keywords

Data ArchitectureETL/ELT DesignApache SparkKafkaAirflow

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data ModelingSchema DesignBatch ProcessingStreaming Data ProcessingData Quality StandardsData GovernanceReal-Time Data ServingOLAP ArchitectureMulti-Tenant ArchitectureData Lifecycle Management

Soft Skills

Strong Communication SkillsCollaboration with Technical Teams

Tools & Technologies

Apache SparkKafkaAirflowSQL

Industry Keywords

Data PlatformLakehouse ArchitectureData MeshData Product Operating ModelsDistributed Data Processing

Tech Stack

Tools & technologies

AirflowApacheETLKafkaSparkSQL

About the role

Key responsibilities & impact

Senior Technical Data Architect
We are looking for a Senior Technical Data Architect to own the technical data architecture end to end, defining how data is ingested, stored, processed, served, and governed across the data platform.
The role is responsible for setting the technical standards and architectural direction that data engineering teams build against, with a strong focus on scalability, reliability, performance, data quality, and maintainability.
Key Responsibilities:
- Own the end-to-end technical architecture of the data platform.
- Define how data is ingested, stored, processed, transformed, served, and governed.
- Design and evolve scalable batch and streaming data architectures.
- Define lakehouse architectures using open table formats and appropriate query engines.
- Design real-time data serving and OLAP architectures for analytical workloads.
- Establish standards for data pipelines, schema evolution, data quality, lineage, and access control.
- Define architectural patterns for ETL/ELT workflows and data processing.
- Guide the use of technologies such as Spark, Kafka, Airflow, and SQL across the data platform.
- Design data platforms that support scale, performance, cost efficiency, and multi-tenancy.
- Evaluate architectural trade-offs and clearly communicate technical decisions.
- Establish reusable architecture patterns and engineering standards across data teams.
- Support the architecture of multiple deployments of the same data platform for different clients.
- Contribute to data mesh and data product operating models where appropriate.
- Work closely with data engineers and technical stakeholders to ensure architectural standards are consistently implemented.
- Identify architectural bottlenecks and drive improvements in reliability, scalability, and data processing efficiency.
- Define technical approaches for schema management, data governance, and data lifecycle management.
- Design architectures suitable for self-managed and on-premises infrastructure environments.

Requirements

What you’ll need

Requirements
- Proven experience as a Data Architect, Technical Data Architect, or similar senior data architecture role.
- Demonstrated ownership of data platform architecture, beyond pipeline development and delivery.
- Strong hands-on experience with both batch and streaming data processing.
- Strong knowledge of data architecture, ETL/ELT, data warehousing, and lakehouse design.
- Hands-on experience with Apache Spark, Kafka, Airflow, and advanced SQL.
- Experience designing lakehouse architectures, open table formats, and query engine integrations.
- Experience designing real-time data serving architectures and OLAP data stores.
- Strong understanding of data modeling, schema design, and schema evolution.
- Experience defining standards for data quality, lineage, governance, and access control.
- Experience designing and operating self-managed or on-premises data infrastructure.
- Strong understanding of distributed data processing and scalable data platforms.
- Experience designing multi-tenant data architectures and understanding the associated isolation and performance trade-offs.
- Ability to evaluate architecture decisions based on scalability, performance, reliability, operational complexity, and cost.
- Experience supporting multiple client deployments of the same data platform is highly desirable.
- Familiarity with data mesh and data product operating models.
- Strong understanding of data lifecycle management and platform reliability.
- Ability to define technical standards and architecture guidelines for data engineering teams.
- Strong communication skills with the ability to explain complex architectural decisions and trade-offs to technical stakeholders.
- Experience collaborating closely with data engineers, platform engineers, and other technical teams.