Vertex Technologies
Middle Data Engineer
- Compensation
- Not disclosed
- Location
- Cairo, Egypt
- Work setup
- Onsite
- Seniority
- Middle
- Employment
- Full time
- Experience
- 2+ years
- Category
- Data engineering
- Posted
Skills
Must have
- Experience: 2+ years of hands-on data engineering (ETL/ELT in production).
- Data Architecture: star/snowflake schemas, SCD, Medallion architecture, and the working vocabulary of the craft: idempotency, backfills, late-arriving data, batch vs streaming trade-offs.
- Key Platforms: proven experience with Databricks (clusters, jobs, Delta) or Snowflake; orchestration with Airflow or ADF.
- Engineering Core: Python (OOP, tests) and SQL for complex transformations - including reading query plans.
- Cloud Fluency: one major cloud and its data services (Kinesis/Lambda, Dataflow/BigQuery, Synapse).
- Infrastructure: Terraform - deploy and modify resources independently.
- Data Quality: dbt tests or Great Expectations in practice; you monitor what you ship.
- Communication: explain technical decisions to clients and the team.
Nice to have
- Advanced IaC: complex Terraform or Pulumi modules.
- Lakehouse Formats: Delta Lake or Iceberg internals - time travel, schema evolution.
- Streaming: a production streaming pipeline you built or maintained.
- NoSQL: data modeling for MongoDB or DynamoDB.
- Certifications: associate level (Azure Data Engineer, Databricks Certified DE).
Full description
Role Summary
You own pipelines end to end: ingestion to modeled warehouse, deployed through CI/CD, monitored, and cost-aware. Projects vary - a lakehouse on one engagement, a classic warehouse on the next - so you work from principles, not from one platform's playbook. Data engineering is your profession, not a side skill picked up on a backend project - and you explain your decisions to engineers and stakeholders alike.
The Mission
Build reliable, transparent, cost-effective data pipelines and help clients understand the value behind our technical choices.
The Tech Stack
- Core Languages: Python (OOP, typing, testing), SQL (advanced). Scala welcome.
- Platforms: Databricks, Snowflake, BigQuery, Synapse, Redshift.
- Orchestration: Airflow or Azure Data Factory; Dagster.
- Transformation: dbt (Core/Cloud), Spark/PySpark (optimization focus).
- Streaming: Kafka, Kinesis, or Event Hubs (working basics).
- Infrastructure: Terraform, Docker, Kubernetes basics, CI/CD.
Your Responsibilities
- Design and build pipelines: ingest, clean, transform into lakes and warehouses.
- Optimize for performance and cost - know a working query from an efficient one (FinOps).
- Deploy through CI/CD and Terraform; keep environments reproducible.
- Implement quality checks and freshness monitoring; alert before the client notices.
- Clarify requirements with stakeholders; review and guide Junior engineers.
What We Offer:
- Long-term career stability with a competitive salary paid in USD.
- Conditions for steady career development.
- Development supported by dedicated mentors and a variety of programs focused on expertise and innovation.
- Private medical insurance provided after successful completion of the probationary period
- A well-equipped and cozy office supports comfort and productivity across all project stages.
- Welcoming atmosphere and a friendly corporate culture.
Requirements
Your Skills
- Experience: 2+ years of hands-on data engineering (ETL/ELT in production).
- Data Architecture: star/snowflake schemas, SCD, Medallion architecture, and the working vocabulary of the craft: idempotency, backfills, late-arriving data, batch vs streaming trade-offs.
- Key Platforms: proven experience with Databricks (clusters, jobs, Delta) or Snowflake; orchestration with Airflow or ADF.