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Integrant

Enterprise software engineering with AI agents

Senior Lead Data Engineer

Data EngineeringConfirmed live in the last 24 hours
Full-timeCairo, Cairo Governorate, EgyptNo salary listed

About the Job

Requirements

  • 10–12 years of experience in data engineering, including hands-on delivery at a senior level.
  • A Bachelor's degree in Computer Science, Computer Engineering, or another quantitative field.
  • Ability to interact with stakeholders at multiple organizational levels, understand analytical challenges, and gather requirements.
  • Excellent written and spoken English and ability to present solutions to technical and business audiences.
  • Proven experience writing technical proposals and solution designs for clients.
  • Deep understanding of analytical architecture evolution, including reporting databases, data warehouses, data lakes, data lakehouses, data fabrics, and data meshes, and ability to technically justify proposed architectures against client needs.
  • Experience mentoring and guiding data engineers.
  • Expert-level SQL and strong programming skills in Python, Scala, or Java for data processing.
  • Extensive experience with at least one enterprise data platform, such as Microsoft SQL Server, Oracle, or Teradata.
  • Hands-on experience implementing on-premises and cloud data warehouses, such as SQL Server, Oracle, Azure Synapse/Fabric, Amazon Redshift, or Google BigQuery.
  • Strong dimensional modeling, including 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 ETL/ELT patterns using tools such as SSIS, Informatica, Talend, Azure Data Factory, AWS Glue, or code-based frameworks such as Spark.
  • Hands-on experience implementing data quality, testing, and observability for data pipelines using tools such as dbt tests, Great Expectations, Monte Carlo, or Soda.
  • Experience building semantic layers and OLAP models using tools such as SSAS, Power BI semantic models, LookML, or dbt semantic layer.
  • Hands-on experience with workflow orchestration using Apache Airflow, Azure Data Factory, AWS Step Functions, or Databricks Workflows.
  • Experience setting up data lakes and lakehouses on cloud object storage such as ADLS Gen2, Amazon S3, or Google Cloud Storage, using open table formats such as Delta Lake, Apache Iceberg, or Apache Hudi.
  • Experience optimizing query and workload performance and setting up high-availability configurations.
  • Experience administering and managing analytical solutions on-premises and in the cloud.
  • Experience building streaming pipelines using Kafka, Kinesis, Event Hubs, Google Pub/Sub, or Spark Structured Streaming.
  • Experience with query federation and data virtualization using Trino/Starburst, Amazon Athena, PolyBase, or BigQuery federated queries.
  • Knowledge of NoSQL databases such as MongoDB, Cosmos DB, DynamoDB, or Cassandra.
  • Knowledge of business intelligence tools such as Power BI, Tableau, Looker, or Qlik.
  • Experience with multiple cloud data platforms, such as Microsoft Azure, AWS, or Google Cloud.
  • Hands-on experience with Snowflake or Databricks.
  • Experience estimating, monitoring, and optimizing cloud data platform costs, including warehouse sizing, compute/storage trade-offs, Snowflake credits, and Databricks DBUs, and reflecting costs in client proposals.
  • Experience implementing data governance and security, including cataloging, lineage, access control, and secrets management, with tools such as Unity Catalog, Microsoft Purview, AWS Lake Formation, cloud IAM, or HashiCorp Vault.
  • Experience with CI/CD and version control for data solutions using GitHub Actions, GitLab CI, or Azure DevOps.
  • Hands-on exposure to LLM and agentic solutions in proof-of-concept or production settings, using tools such as LangGraph, AutoGen, CrewAI, LlamaIndex, or n8n.
  • Experience with AI-assisted development tools such as Cursor, Codex, or Claude Code.
  • Familiarity with DataOps.

Responsibilities

  • Design data and AI solutions end to end and personally implement critical components.
  • Design solutions for data ingestion, storage, processing, transformation, enrichment, and presentation for analytical consumption.
  • Engage clients at multiple levels to elicit requirements, assess analytical challenges, and develop technical proposals and solution designs.
  • Select and customize analytical architectures, including data warehouses, data lakes, data lakehouses, data fabrics, and data meshes, and justify how they meet client needs.
  • Design and build agentic and LLM-powered solutions on 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 on current architectures and methodologies, including DataOps and MLOps.
  • Mentor engineers on the team.

Desired Qualifications

  • Familiarity with Agent Protocols such as MCP.
  • Familiarity with transformation frameworks such as dbt.
  • Familiarity with MLOps and ML platforms such as MLflow, Azure ML, Amazon SageMaker, or Vertex AI.
  • Familiarity with cloud AI services such as Azure AI services, Amazon Bedrock, or Vertex AI.
  • Familiarity with containerization tools and frameworks such as Docker and Kubernetes.
  • Familiarity with the big data ecosystem, including Spark and Hive, on platforms such as Databricks, Amazon EMR, Dataproc, or HDInsight.
  • Familiarity with serverless functions for lightweight transformations, such as AWS Lambda, Azure Functions, or Google Cloud Functions.

About the Company

Integrant

Enterprise software engineering with AI agents

Integrant builds enterprise software for regulated industries like medical devices, life sciences, pharma, financial services, manufacturing, and the public sector. Their offerings include full platform development, modernization of legacy systems, or embedding AI agents into client workflows. It uses the Agentic Software Development Life Cycle (ADLC), where AI agents assist across requirements, architecture, build, test, and deployment, while engineers with deep domain knowledge govern every step to ensure quality. Unlike many firms, Integrant combines top-tier engineering talent (the top 1% in a demanding market), a long-track record in regulated environments, and responsible AI acceleration with engineer accountability. Their goal is to be the trusted technology partner clients rely on for mission-critical software that lasts. Headquartered in San Diego with engineering centers in Cairo and Amman.

Company size
201-500
Company stage
Not listed
Total funding
Not listed
Headquarters
San Diego, CA
Founded
1992

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