Senior Data Engineer

Bosta

Senior Data Engineer responsible for end-to-end reconstruction of the data platform. Focusing on MongoDB, ingestion patterns, and semantic layers for efficient BI/AI consumption.

Posted 5/25/2026full-timeCairo • 🇪🇬 EgyptSeniorWebsite

Tech Stack

Tools & technologies

AirflowAmazon RedshiftBigQueryCloudKafkaLinuxMongoDBPythonSQLTableau

About the role

Key responsibilities & impact

End-to-end pipeline work: MongoDB CDC → ingestion → lakehouse → warehouse → dbt → semantic layer → BI/AI consumers
Co-ownership of architecture decisions with the Data Engineering Lead
CDC from production MongoDB without degrading operational DB performance; ingestion patterns that make adding a new source a config change, not a project
Orchestration that’s observable end-to-end (Airflow, Dagster, or Prefect)
The dbt project: structure, conventions, tests, contracts, exposures, CI
The semantic / metrics layer (dbt Semantic Layer, Cube, or equivalent) — one canonical definition per business metric
LLM-readiness: column-level documentation, PII tagging, query cost guardrails, materialized metric tables, and evals on AI-generated SQL
Migration of existing logic out of the Tableau and Metabase sprawl into modeled, governed sources

Requirements

What you’ll need

4+ years across data engineering and/or analytics engineering — you’ve spent meaningful time on both sides
Comfort spanning the stack: comfortable shipping a Debezium connector one week and a dbt mart the next
Deep dbt and SQL — you’ve owned a non-trivial project, not just contributed to one
Production CDC experience (Debezium, Kafka Connect, Airbyte, or hand-rolled) against operational databases — bonus if that database was MongoDB
A cloud warehouse you know deeply (Redshift, Snowflake, BigQuery, or Databricks)
Strong Python; comfortable in Linux, infra-as-code, and CI/CD
Working understanding of how LLM tooling (RAG, NL-to-SQL, embedded agents) consumes a data platform — and what breaks when the platform isn’t ready
Strong opinions on modeling, lightly held; bias toward observability
**Bonus:**
MongoDB schema evolution at scale
Production semantic-layer rollouts (dbt Semantic Layer, Cube, LookML, MetricFlow)
Lakehouse formats (Iceberg, Delta, Hudi) or streaming experience (Kafka, Flink, Kinesis)
Data catalog / lineage tooling (DataHub, Atlan, Collibra)
Logistics, marketplaces, or other operationally heavy domains
Evals on AI-generated SQL or analytical reasoning

Benefits

Comp & perks

🌐 Worldwide ❌ Jobs You've Hidden ⭐️ Saved Jobs ✅ Applied Jobs ✉️ Email Alerts 👤 Account Bosta Website LinkedIn All Job Openings 201 - 500 employees Launched in 2017, Bosta is an overnight delivery provider for E-commerce companies in Egypt & Saudi Arabia. Leveraging technology to empower businesses is what we do best. Our goal is to disrupt the logistics industry by bringing technology and experience altogether to provide a one-stop-shop for delivery solutions. Senior Data Engineer 🔥 1 hour ago 🏢🏡 Cairo – Hybrid ⏰ Full Time 🟠 Senior 🚰 Data Engineer Airflow Amazon Redshift BigQuery Cloud Kafka Linux MongoDB Python SQL Tableau Apply Now Find Hiring Managers Customize resume + cover letter Report problem ☆ Save ☑️ Mark as applied ❌ Hide 📋 Description
End-to-end pipeline work: MongoDB CDC → ingestion → lakehouse → warehouse → dbt → semantic layer → BI/AI consumers
Co-ownership of architecture decisions with the Data Engineering Lead
CDC from production MongoDB without degrading operational DB performance; ingestion patterns that make adding a new source a config change, not a project
Orchestration that’s observable end-to-end (Airflow, Dagster, or Prefect)
The dbt project: structure, conventions, tests, contracts, exposures, CI
The semantic / metrics layer (dbt Semantic Layer, Cube, or equivalent) — one canonical definition per business metric
LLM-readiness: column-level documentation, PII tagging, query cost guardrails, materialized metric tables, and evals on AI-generated SQL
Migration of existing logic out of the Tableau and Metabase sprawl into modeled, governed sources 🎯 Requirements
4+ years across data engineering and/or analytics engineering — you’ve spent meaningful time on both sides
Comfort spanning the stack: comfortable shipping a Debezium connector one week and a dbt mart the next
Deep dbt and SQL — you’ve owned a non-trivial project, not just contributed to one
Production CDC experience (Debezium, Kafka Connect, Airbyte, or hand-rolled) against operational databases — bonus if that database was MongoDB
A cloud warehouse you know deeply (Redshift, Snowflake, BigQuery, or Databricks)
Strong Python; comfortable in Linux, infra-as-code, and CI/CD
Working understanding of how LLM tooling (RAG, NL-to-SQL, embedded agents) consumes a data platform — and what breaks when the platform isn’t ready
Strong opinions on modeling, lightly held; bias toward observability
**Bonus:**
MongoDB schema evolution at scale
Production semantic-layer rollouts (dbt Semantic Layer, Cube, LookML, MetricFlow)
Lakehouse formats (Iceberg, Delta, Hudi) or streaming experience (Kafka, Flink, Kinesis)
Data catalog / lineage tooling (DataHub, Atlan, Collibra)
Logistics, marketplaces, or other operationally heavy domains
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Hard Skills & Tools

data engineeringanalytics engineeringdbtSQLproduction CDCPythonLinuxinfra-as-codeCI/CDMongoDB

Soft Skills

co-ownershipstrong opinions on modelingbias toward observability