Design, build, and maintain scalable ETL/ELT pipelines and data models for operational systems and enterprise data warehouses. Optimize performance, implement data quality and governance, support production workflows, and collaborate with analysts, scientists, DBAs, and product teams to deliver reliable datasets for analytics and reporting.
Position:Senior Data EngineerJob Description:
Must-Have Requirements
• 5+ years of experience as a Data Engineer or similar role.
• Strong Python Skills and Experience
• Strong SQL /PLSQL skills and experience with relational databases (Oracle, PostgreSQL, MySQL, or similar).
• Experience with NoSQL databases and streaming systems (Kafka, Kinesis, etc.).
• Hands-on experience with data processing frameworks (Spark, Databricks, or similar).
• Proficiency with cloud platforms (AWS).
• Experience with workflow orchestration (Airflow, Prefect, or similar).
• Strong understanding of ETL/ELT patterns, data warehousing concepts, and BI fundamentals.
• Proven ability to design robust data models and optimize database structures.
• Knowledge of data governance, cataloging, and metadata management.
Location:EG-Cairo, Egypt (Al Emdad & Al Tamween)Time Type:Full timeJob Category:Information Technology
Seeking an experienced Senior Data Engineer to design, development, and optimization of
our data pipelines, data models, and analytics infrastructure. The ideal candidate has
strong experience in building scalable data platforms across operational systems and
enterprise data warehouses.
Key Responsibilities
Core Responsibilities
• Design, build, and maintain scalable ETL/ELT pipelines for structured and unstructured data.
• Develop high-quality data integration workflows using modern data engineering tools and frameworks.
• Optimize data processing performance, storage usage, and pipeline reliability.
• Implement data quality checks, validation rules, and monitoring dashboards.
• Collaborate with data analysts, data scientists, and business teams to deliver reliable datasets suitable for reporting and analytics.
• Manage data security, access controls, and governance best practices.
• Support production workflows, troubleshoot issues, and improve platform stability.
Additional Responsibilities for Data Models & Structures (Operational + DW)
• Own the design and maintenance of logical and physical data models for
operational systems and enterprise data warehouse (OLTP + DWH).
• Define and enforce data modeling standards, naming conventions, and schema governance.
• Translate business processes into efficient database schemas, including star/snowflake models.
• Optimize queries and database structures for high-performance analytics and real-time data access.
• Work closely with DBAs, architects, and product teams to ensure data models meet scalability and performance requirements.
our data pipelines, data models, and analytics infrastructure. The ideal candidate has
strong experience in building scalable data platforms across operational systems and
enterprise data warehouses.
Key Responsibilities
Core Responsibilities
• Design, build, and maintain scalable ETL/ELT pipelines for structured and unstructured data.
• Develop high-quality data integration workflows using modern data engineering tools and frameworks.
• Optimize data processing performance, storage usage, and pipeline reliability.
• Implement data quality checks, validation rules, and monitoring dashboards.
• Collaborate with data analysts, data scientists, and business teams to deliver reliable datasets suitable for reporting and analytics.
• Manage data security, access controls, and governance best practices.
• Support production workflows, troubleshoot issues, and improve platform stability.
Additional Responsibilities for Data Models & Structures (Operational + DW)
• Own the design and maintenance of logical and physical data models for
operational systems and enterprise data warehouse (OLTP + DWH).
• Define and enforce data modeling standards, naming conventions, and schema governance.
• Translate business processes into efficient database schemas, including star/snowflake models.
• Optimize queries and database structures for high-performance analytics and real-time data access.
• Work closely with DBAs, architects, and product teams to ensure data models meet scalability and performance requirements.
• Lead database refactoring efforts, schema evolution, and versioning processes.
• Perform impact analysis for schema changes across upstream and downstream systems.
Requirements
• Perform impact analysis for schema changes across upstream and downstream systems.
Requirements
Must-Have Requirements
• 5+ years of experience as a Data Engineer or similar role.
• Strong Python Skills and Experience
• Strong SQL /PLSQL skills and experience with relational databases (Oracle, PostgreSQL, MySQL, or similar).
• Experience with NoSQL databases and streaming systems (Kafka, Kinesis, etc.).
• Hands-on experience with data processing frameworks (Spark, Databricks, or similar).
• Proficiency with cloud platforms (AWS).
• Experience with workflow orchestration (Airflow, Prefect, or similar).
• Strong understanding of ETL/ELT patterns, data warehousing concepts, and BI fundamentals.
• Proven ability to design robust data models and optimize database structures.
• Knowledge of data governance, cataloging, and metadata management.
Nice-to-Have Skills
• Familiarity with CI/CD, infrastructure-as-code, and DevOps practices.
• Experience with real-time analytics or high-volume distributed data systems
• Familiarity with CI/CD, infrastructure-as-code, and DevOps practices.
• Experience with real-time analytics or high-volume distributed data systems
Similar Jobs
Information Technology
Design, build, and operate scalable cloud-native data pipelines and infrastructure to support analytics and AI workloads. Develop dimensional and semantic data models and curated data products, optimize cloud data services, maintain versioned development workflows, and collaborate with AI, Analytics, and Product teams to deliver high-quality data for LLMs, agents, and RAG solutions.
Top Skills:
Ai AgentsAirflowAthenaAWSDbtGitGlueLambdaLlmsPrefectPythonRagRedshiftS3SQL
Information Technology
Design, build, and maintain scalable batch and near-real-time data pipelines and models. Ensure data quality, observability, security, and cost-efficient performance. Partner with cross-functional teams to enable analytics, support BI use cases, and mentor junior engineers while applying DevOps practices and incident management.
Top Skills:
AirflowAPIsAWSAzureBigQueryCdcCi/CdDagsterDatabricksDbtDelta LakeDockerFivetranFlinkGCPGitIcebergJavaKafkaKubernetesLookerMetabasePower BIPrefectPythonRedshiftScalaSnowflakeSparkSQLStitchSupersetTableauTerraformWebhooks
Information Technology • Machine Learning • Real Estate
Design, build, and maintain scalable data pipelines, data marts, and schemas; collaborate with data scientists and stakeholders; deploy and maintain data storage and cloud infrastructure; implement data validation and quality checks; optimize pipeline performance and evaluate new tools to improve data engineering capabilities.
Top Skills:
APIsAWSAzureData WarehousesDatabasesFlinkGoogle Cloud PlatformKafkaPythonSpark StreamingSQL
What you need to know about the Austin Tech Scene
Austin has a diverse and thriving tech ecosystem thanks to home-grown companies like Dell and major campuses for IBM, AMD and Apple. The state’s flagship university, the University of Texas at Austin, is known for its engineering school, and the city is known for its annual South by Southwest tech and media conference. Austin’s tech scene spans many verticals, but it’s particularly known for hardware, including semiconductors, as well as AI, biotechnology and cloud computing. And its food and music scene, low taxes and favorable climate has made the city a destination for tech workers from across the country.
Key Facts About Austin Tech
- Number of Tech Workers: 180,500; 13.7% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Dell, IBM, AMD, Apple, Alphabet
- Key Industries: Artificial intelligence, hardware, cloud computing, software, healthtech
- Funding Landscape: $4.5 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Live Oak Ventures, Austin Ventures, Hinge Capital, Gigafund, KdT Ventures, Next Coast Ventures, Silverton Partners
- Research Centers and Universities: University of Texas, Southwestern University, Texas State University, Center for Complex Quantum Systems, Oden Institute for Computational Engineering and Sciences, Texas Advanced Computing Center
