Data Engineering Senior Manager
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Before you apply
LocationGiza
SenioritySenior
Experience10+ yrs
Company size1,001–5,000 people
First seenOct 22, 2026 · 2w ago
Verified live2d ago
At a glanceSummarised by Seekless from the posting.
10+ years of experience in Data Architecture, Data Engineering, and Data Modeling leading large-scale enterprise data initiatives
Expertise in designing, implementing, and optimizing complex data ecosystems in hybrid environments
Strong background in data governance, security, compliance, and risk management across enterprise systems
Hands-on experience managing data integration, migration, and transformation projects in a large-scale business setting
Deep expertise in architecting, optimizing, and managing relational databases (Oracle, PostgreSQL)
Deep expertise in NoSQL databases (MongoDB, Cassandra)
Proven track record of designing and implementing scalable, enterprise-level Data Warehouse (DWH) architectures
Expert-level proficiency in writing and tuning complex SQL queries, stored procedures, functions, and triggers for high-performance data processing
Strong expertise in data modeling techniques (conceptual, logical, physical, dimensional, and semantic models)
Deep understanding of data integration patterns, streaming data platforms, and event-driven architectures, and quality frameworks
Proficiency with cloud data platforms (AWS, Azure, GCP) and modern ecosystem tools (Databricks, Snowflake, BigQuery, Synapse)
Strong command of ETL/ELT frameworks, APIs, and microservices for data delivery
Proven expertise in architecting and managing data lakes and data warehouses for big data environments
Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field
Fluent in English
Master's degree in Computer Science, Engineering, Information Systems, or a related field
Certified Data Management Professional (CDMP) preferred
Microsoft Certified: Azure Data Engineer Associate or AWS Certified Data Engineer Associate preferred
Skills
Oracle
PostgreSQL
MongoDB
Cassandra
SQL
AWS
Azure
GCP
Databricks
Snowflake
BigQuery
Synapse
ETL
ELT
API
microservices
About the role
Responsible for leading and managing the organization’s data engineering function, ensuring the development and delivery of scalable, reliable, and high-performing data platforms and pipelines aligned with business objectives. This role focuses on defining data engineering standards, practices, and frameworks to enable efficient data ingestion, transformation, integration, and processing across cloud and on-premises environments. The Data Engineering Manager leads and mentors data engineers, oversees engineering projects and delivery, and collaborates closely with data architecture, governance, analytics, and business teams to ensure data solutions are secure, governed, high-quality, and optimized for performance and scalability.
Responsibilities
Data Architecture & Design:
· Develop and maintain a scalable, secure, and high-performance data architecture aligned with business needs.
· Ensure seamless data flow between systems, integrating structured and unstructured data sources.
· Define and implement best practices for data storage, processing, and security.
Select and optimize data platforms, tools, and technologies for efficiency and cost-effectiveness.
Data Modeling:
· Design and implement data models (dimensional, relational, Data Vault, NoSQL) to support operational and analytical use cases.
· Ensure consistency, accuracy, and integrity of data models across the organization.
· Continuously refine models to improve performance and adaptability to evolving business requirements.
Establish and enforce data modeling standards and governance.
Data Engineering and Pipeline Development:
· Build, optimize, and maintain automated ETL/ELT pipelines to process large-scale data.
· Troubleshoot, debug and upgrade existing ETL solutions.
· Enhance data infrastructure to support efficient storage, retrieval, and transformation.
· Implement automation to reduce manual processes and increase workflow efficiency.
· Ensure data pipelines are robust, scalable, and support real-time and batch processing.
Identify data discrepancies and develop data metrics.
Innovation & Technology Adoption:
· Stay up to date with the latest trends in data architecture, engineering, and analytics to identify and implement innovative solutions.
· Research and integrate cutting-edge tools and platforms to enhance data management capabilities and streamline processes.
· Encourage a team culture focused on continuous improvement, supporting experimentation with new technologies and methodologies.
Develop and execute a technology roadmap to ensure the team is using the best tools available to meet both current and future data needs.
Optimization & Performance Management:
· Define and enforce data governance policies, ensuring compliance with GDPR, and other regulations.
· Monitor and optimize database performance, query execution, and storage efficiency.
· Manage data quality, metadata, and lineage to ensure reliability and transparency.
· Implement proactive monitoring and alerting for data infrastructure health.
Monitor performance and quality control plans to identify improvements.
Collaboration with Cross-Functional Teams:
· Work closely with data scientists, business analysts, and BI teams to understand data requirements and design solutions that enable advanced analytics, reporting, and decision-making.
· Act as a bridge between business units and IT, ensuring that data architecture and engineering solutions meet both technical and business needs.
Lead efforts to optimize data workflows and processes across departments, driving efficiency and alignment in data operations.
Development of Employee’s Performance:
· Provides support for professional development of direct reports.
· Direct the hiring, or performance evaluation of the team and oversee their daily activities.
· Motivate, organize and encourage teamwork within the workforce to ensure targets are met or exceeded.
· Monitors Training Effectiveness
Reports on Training Activities
Requirements
Educational Requirements: Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or a related field.
Advanced degrees provide deeper technical and strategic insights, enabling the professional to drive complex data solutions and innovations at the organizational level.
Special Certification or Training Required:
Nice to have
•
Certified Data Management Professional (CDMP) – Preferred for expertise in data architecture and governance is preferred
•
Microsoft Certified: Azure Data Engineer Associate or AWS Certified Data Engineer Associate is preferred.
Required Industry Experience:
•
10+ years of experience in Data Architecture, Data Engineering, and Data Modeling with a proven track record of leading large-scale enterprise data initiatives.
•
Expertise in designing, implementing, and optimizing complex data ecosystems in hybrid environments.
•
Strong background in data governance, security, compliance, and risk management across enterprise systems.
•
Hands-on experience managing data integration, migration, and transformation projects in a large-scale business setting.
Technological Requirements:
1.
Deep expertise in architecting, optimizing, and managing both relational (e.g., Oracle, PostgreSQL) and NoSQL (e.g., MongoDB, Cassandra) databases.
2.
Proven track record of designing and implementing scalable, enterprise-level Data Warehouse (DWH) architectures.
3.
Expert-level proficiency in writing and tuning complex SQL queries, stored procedures, functions, and triggers for high-performance data processing.
4.
Strong expertise in data modeling techniques (conceptual, logical, physical, dimensional, and semantic models).
5.
Deep understanding of data integration patterns, streaming data platforms, and event-driven architectures, and quality frameworks.
6.
Proficiency with cloud data platforms (AWS, Azure, GCP) and modern ecosystem tools (Databricks, Snowflake, BigQuery, Synapse).
7.
Strong command of ETL/ELT frameworks, APIs, and microservices for data delivery.
8.
Proven expertise in architecting and managing data lakes and data warehouses for big data environments.
Language Requirements: Fluent in English.