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Integrant

Senior Lead Data Engineer

LocationCairo, Cairo Governorate, Egypt
Typefull-time
SeniorityLead
Experience10–12 yrs
DepartmentIT
Company size26+ people
First seenOct 8, 2026 · 3d ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
Must have31
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
Ability to interact with stakeholders at multiple levels within a given organization, understand current analytical challenges and gather requirements
Excellent written and spoken English
Proven experience writing technical proposals and solution designs for clients
Deep understanding of the evolution of analytical architectures from reporting databases to data warehouses, data lakes, data lakehouses, data fabrics, data meshes and beyond
Experience mentoring and guiding data engineers
Expert-level SQL
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)
Hands-on experience implementing data quality, testing and observability for data pipelines (e.g. dbt tests, Great Expectations, Monte Carlo, Soda)
Experience building semantic layers and OLAP models (e.g. SSAS, Power BI semantic models, LookML, dbt semantic layer)
Hands-on experience with workflow orchestration (e.g. Apache Airflow, Azure Data Factory, AWS Step Functions, Databricks Workflows)
Experience setting up data lakes and lakehouses on cloud object storage (e.g. ADLS Gen2, Amazon S3, Google Cloud Storage) using open table formats (e.g. Delta Lake, Apache Iceberg, Apache Hudi)
Experience optimizing query and workload performance and setting up high availability configurations
Experience administering and managing analytical solutions both on-premises and in the cloud
Experience building streaming pipelines (e.g. Kafka, Kinesis, Event Hubs, Google Pub/Sub, Spark Structured Streaming)
Experience with query federation and data virtualization (e.g. Trino/Starburst, Amazon Athena, PolyBase, BigQuery federated queries)
Knowledge of NoSQL databases (e.g. MongoDB, Cosmos DB, DynamoDB, Cassandra)
Knowledge of BI tools (e.g. Power BI, Tableau, Looker, Qlik)
Experience with multiple cloud data platforms (e.g. Microsoft Azure, AWS, Google Cloud)
Hands-on experience with Snowflake or Databricks
Experience estimating, monitoring and optimizing cloud data platform costs (e.g. warehouse sizing, compute/storage trade-offs, Snowflake credits, Databricks DBUs), and reflecting them in client proposals
Experience implementing data governance and security: cataloging, lineage, access control and secrets management (e.g. Unity Catalog, Microsoft Purview, AWS Lake Formation, Collibra, cloud IAM, HashiCorp Vault)
Experience with CI/CD and version control for data solutions (e.g. GitHub Actions, GitLab CI, Azure DevOps)
Hands-on exposure to LLM and agentic solutions, in PoC or production (e.g. LangGraph/AutoGen/CrewAI/LlamaIndex/n8n)
Experience with AI-assisted development (e.g. Cursor/Codex/Claude Code)
Familiarity with DataOps
Nice to have8
Post Graduate degrees preferable
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)
Skills
SQL
Python
Scala
Java
Microsoft SQL Server
Oracle
Teradata
Azure Synapse
Fabric
Amazon Redshift
Google BigQuery
SSIS
Informatica
Talend
Azure Data Factory
AWS Glue
Spark
dbt
Great Expectations
Monte Carlo
Soda
SSAS
Power BI
LookML
Apache Airflow
AWS Step Functions
Databricks Workflows
ADLS Gen2
Amazon S3
Google Cloud Storage
Delta Lake
Apache Iceberg
Apache Hudi
Kafka
Kinesis
Event Hubs
Google Pub/Sub
Spark Structured Streaming
Trino
Starburst
Amazon Athena
PolyBase
MongoDB
Cosmos DB
DynamoDB
Cassandra
Tableau
Looker
Qlik
Snowflake
Databricks
Unity Catalog
Microsoft Purview
AWS Lake Formation
Collibra
HashiCorp Vault
GitHub Actions
GitLab CI
Azure DevOps
LangGraph
AutoGen
CrewAI
LlamaIndex
n8n
Cursor
Codex
Claude Code
MCP
MLflow
Azure ML
Amazon SageMaker
Vertex AI
Docker
Kubernetes
Hive
Amazon EMR
Dataproc
HDInsight
AWS Lambda
Azure Functions
Google Cloud Functions
Benefits
Salary paid in usd
Career progression reviews every six months
Supportive and friendly work environment
Premium medical insurance [employee + family]
Social insurance
English language development courses
Interest free loans paid over 2.5 years
Technical development courses
Employment referral program
Premium location in maadi
About the role
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.
Responsibilities
•
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.
Requirements
Architecture & 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).
•
Hands-on experience implementing data quality, testing and observability for data pipelines (e.g. dbt tests, Great Expectations, Monte Carlo, Soda).
•
Experience building semantic layers and OLAP models (e.g. SSAS, Power BI semantic models, LookML, dbt semantic layer).
•
Hands-on experience with workflow orchestration (e.g. Apache Airflow, Azure Data Factory, AWS Step Functions, Databricks Workflows).
•
Experience setting up data lakes and lakehouses on cloud object storage (e.g. ADLS Gen2, Amazon S3, Google Cloud Storage) using open table formats (e.g. Delta Lake, Apache Iceberg, Apache Hudi).
•
Experience optimizing query and workload performance and setting up high availability configurations.
•
Experience administering and managing analytical solutions both on-premises and in the cloud.
•
Experience building streaming pipelines (e.g. Kafka, Kinesis, Event Hubs, Google Pub/Sub, Spark Structured Streaming).
•
Experience with query federation and data virtualization (e.g. Trino/Starburst, Amazon Athena, PolyBase, BigQuery federated queries).
•
Knowledge of NoSQL databases (e.g. MongoDB, Cosmos DB, DynamoDB, Cassandra).
•
Knowledge of BI tools (e.g. Power BI, Tableau, Looker, Qlik).
Cloud, Platforms & Governance
•
Experience with multiple cloud data platforms (e.g. Microsoft Azure, AWS, Google Cloud).
•
Hands-on experience with Snowflake or Databricks.
•
Experience estimating, monitoring and optimizing cloud data platform costs (e.g. warehouse sizing, compute/storage trade-offs, Snowflake credits, Databricks DBUs), and reflecting them in client proposals.
•
Experience implementing data governance and security: cataloging, lineage, access control and secrets management (e.g. Unity Catalog, Microsoft Purview, AWS Lake Formation, Collibra, cloud IAM, HashiCorp Vault).
•
Experience with CI/CD and version control for data solutions (e.g. GitHub Actions, GitLab CI, Azure DevOps).
AI & Agentic
•
Hands-on exposure to LLM and agentic solutions, in PoC or production (e.g. LangGraph/AutoGen/CrewAI/LlamaIndex/n8n).
•
Experience with AI-assisted development (e.g. Cursor/Codex/Claude Code).
•
Familiarity with DataOps.
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).
Benefits
•
Salary paid in USD
•
Career progression reviews every six months
•
Supportive and friendly work environment
•
Premium medical insurance [employee + family]
•
Social insurance
•
English language development courses
•
Interest-free loans paid over 2.5 years
•
Technical development courses
•
Employment referral program
•
Premium location in Maadi
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