Data Engineer

• Own the end-to-end data platform architecture and define technical standards for data engineering teams
• Design and implement batch and real-time/streaming data architectures
• Define Lakehouse architecture, open table formats, and query engine solutions
• Design real-time data serving and OLAP solutions
• Establish standards for ETL pipelines, schema evolution, data quality, data lineage, and access control
• Design scalable, cost-effective, and multi-tenant data platforms
• Work with self-managed and on-premises infrastructure
• Collaborate with data engineering and technical teams to ensure architecture standards are properly implemented

Senior Technical Data Architect – Telco, Banking

• Build and run large scale batch data pipelines.
• Keep data processing jobs fast and affordable as data volumes grow.
• Build and manage large scale Apache Spark data processing jobs.
• Perform performance tuning to maintain efficient and reliable pipeline execution.
• Support data modelling for analytics.
• Organise data in a lakehouse for downstream use.
• Handle automated scheduling, monitoring, and data quality checks.
• Work with platform and product teams on end to end data flows.

Senior Data Engineer, Spark

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.

Senior Data Architect - CVM & Marketing Analytics

Description
Builds and runs large scale batch data pipelines, keeping jobs fast and affordable as data volumes grow. Works with Apache Spark to build and manage large scale data processing jobs, including performance tuning to maintain efficient and reliable pipeline execution. Supports data modelling for analytics and organises data in a lakehouse so it is usable downstream. Handles automated scheduling, monitoring, and data quality checks, while working with platform and product teams on end to end data flows.

Senior Data Engineer (Spark)

• Build and run real-time streaming pipelines
• Keep latency low and jobs recoverable at high volume
• Monitor pipeline health
• Handle recovery when jobs fail

Senior Data Engineer, Flink

• Own the customer data use case library and define the commercial purpose of each use case
• Translate business objectives into customer attributes, segments, and actionable data requirements
• Design and define customer segmentation strategies for targeted campaigns and customer lifecycle management
• Own use cases end to end, including churn, retention, dormancy, win-back, upsell, tariff migration, and device upgrade
• Define customer base management strategies and identify high-value customer segments
• Design campaign targeting criteria and ensure segments are practical and commercially actionable
• Work with customer data and campaign management platforms to build and activate segments
• Define measurement frameworks for campaign performance, customer response, and incremental impact
• Design control groups and measurement approaches to evaluate campaign incrementality
• Analyze customer behavior and identify opportunities for retention, cross-sell, upsell, and reactivation
• Work closely with engineering and data teams to specify required customer attributes, segment logic, and data definitions
• Challenge unnecessary segmentation and prioritize use cases that can be practically activated and measured
• Ensure customer segments have clear business definitions, eligibility criteria, and measurable outcomes
• Support the development of reusable customer data and campaign use cases across banking products and customer journeys
• Translate analytical insights into clear requirements for campaign and customer data teams

Senior Data Architect – Banking

• Own and define the end-to-end telco data model, covering source systems, canonical entities, relationships, attributes, and business definitions
• Lead data discovery and requirements workshops with client stakeholders across business and technology teams
• Map data from BSS and OSS source systems into a canonical data model
• Define relationships between Party, Customer, Account, Subscriber, Service, Product, and Resource entities, including complex B2B, household, and family-plan scenarios
• Design and document identity resolution rules around MSISDN, customer, subscriber, SIM, and account relationships
• Address telecom-specific identity scenarios including number portability, SIM swaps, and number recycling
• Work with telecom usage and charging data, including CDRs/xDRs, mediation, rating, charging, and billing structures
• Develop detailed data mapping specifications, transformation rules, data definitions, and implementation-ready documentation
• Collaborate with data engineering, architecture, product, BSS/OSS, and business teams to ensure the data model can be implemented effectively
• Support the design and implementation of Customer Data Platforms, Single Customer View, and customer 360 solutions
• Ensure data architecture aligns with relevant telecom standards and enterprise data governance requirements

Data Engineer jobs in Egypt