Integrant
Senior Lead Data Engineer
- Compensation
- Not disclosed
- Location
- Cairo, Cairo Governorate, Egypt
- Work setup
- Not specified
- Seniority
- Lead
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 10-12 years of experience in data engineering, including hands-on delivery at a senior level.
- Bachelor's degree in Computer Science, Computer Engineering or other quantitative field. Post Graduate degrees preferable.
- The ability to interact with stakeholders at multiple levels within a given organization, understand current analytical challenges and gather requirements.
- Excellent written and spoken English, with the ability to present solutions to technical and business audiences.
- Proven experience writing technical proposals and solution designs for clients.
- A deep understanding of the evolution of analytical architectures from reporting databases to data warehouses, data lakes, data lakehouses, data fabrics, data meshes and beyond, with the ability to te
- Experience mentoring and guiding data engineers.
Nice to have
- Familiarity with Agent Protocols (e.g. MCP).
- Familiarity with transformation frameworks (e.g. dbt).
- Familiarity with MLOps and ML platforms (e.g. MLflow, Azure ML, Amazon SageMaker, Vertex AI).
- Familiarity with cloud AI services (e.g. Azure AI services, Amazon Bedrock, Vertex AI).
- Familiarity with containerization tools and frameworks (Docker, Kubernetes, etc.).
- Familiarity with the big data ecosystem, including Spark and Hive (e.g. on Databricks, Amazon EMR, Dataproc, HDInsight).
- Familiarity with serverless functions for lightweight transformations (e.g. AWS Lambda, Azure Functions, Google Cloud Functions).
Full description
Integrant is looking for game changers to join our team as a "Senior Lead Data Engineer". This is a senior, hands-on leader who designs data and AI solutions end to end and builds them. You'll be responsible for designing solutions for the ingestion, storage, processing, transformation, enrichment, and presentation of data for analytical consumption, and for personally implementing the critical parts of those solutions.
- Engage clients at multiple levels to elicit requirements, assess their current analytical challenges, and turn them into technical proposals and solution designs.
- Select and customize analytical architectures (Data Warehouses, Data Lakes, Data Lakehouses, Data Fabrics, Data Meshes, etc.) and technically justify how they meet each client's needs.
- Design and build agentic and LLM-powered solutions on top of modern data platforms.
- Lead hands-on implementation of data pipelines, warehouses, and lakes, from design through performance tuning and production support.
- Build data strategies, and coach clients and internal teams in the latest architectures and methodologies (DataOps, MLOps, etc.), including mentoring engineers on the team.
RequirementsArchitecture & Leadership
- 10-12 years of experience in data engineering, including hands-on delivery at a senior level.
- Bachelor's degree in Computer Science, Computer Engineering or other quantitative field. Post Graduate degrees preferable.
- The ability to interact with stakeholders at multiple levels within a given organization, understand current analytical challenges and gather requirements.
- Excellent written and spoken English, with the ability to present solutions to technical and business audiences.
- Proven experience writing technical proposals and solution designs for clients.
- A deep understanding of the evolution of analytical architectures from reporting databases to data warehouses, data lakes, data lakehouses, data fabrics, data meshes and beyond, with the ability to technically justify how a proposed architecture meets a client's analytical needs.
- Experience mentoring and guiding data engineers.
Hands-on Data Engineering
- Expert-level SQL and strong programming skills in Python (or Scala/Java) for data processing.
- Extensive experience with at least one enterprise data platform (e.g. Microsoft SQL Server stack, Oracle, Teradata).
- Hands-on experience implementing data warehouses both on-premises and in the cloud (e.g. SQL Server, Oracle, Azure Synapse/Fabric, Amazon Redshift, Google BigQuery).
- Strong dimensional modeling: Dimensions, Facts, Slowly Changing Dimensions, Outriggers, Role-Playing, Junk, Degenerate and Multi-valued Dimensions; Transactional, Periodic Snapshot and Accumulating Snapshot Facts.
- Hands-on experience implementing common ETL/ELT patterns using tools such as SSIS, Informatica, Talend, Azure Data Factory, AWS Glue, or code-based frameworks (e.g. Spark).