Senior Python Data Engineer — Pipelines & Data Quality

Recruitment Intelligence; Islamabad; On-site; PKR 2,000,000 - 4,200,000; Full time

Benefits offered by this job

Medical benefits for you and family

Free lunch

Annual performance-tied increments

Lean and agile work culture

Job summary

Convo is seeking a Python Data Engineer with 3+ years of production data engineering experience. You will maintain and enhance Python/SQL pipelines orchestrated via Airflow or Azure Data Factory, implement quality and schema checks, and troubleshoot pipeline issues to ensure reliable data flows.

You will also monitor data freshness, perform incident triage, support Spark jobs, and contribute to documentation and runbooks for operational stability.

Qualifications

• 3+ years in production data engineering or data-pipeline operations.
• Strong Python and SQL for data engineering.
• Experience with Airflow, Azure Data Factory or equivalent.
• Knowledge of Spark and distributed data-processing concepts.
• Data-quality and schema-validation implementation.
• Monitoring, incident triage and root-cause analysis.

Responsibilities

• Maintain and enhance Python/SQL pipelines orchestrated through Airflow, Azure Data Factory or equivalent.
• Implement schema, completeness, freshness and reconciliation checks.
• Diagnose and resolve pipeline failures, data-quality incidents and performance regressions.
• Support Spark jobs and incremental data-processing patterns.
• Add monitoring, structured logging and actionable alerts.
• Document fixes, recurring failure patterns and operational procedures.

Job description

Convo is seeking a Python Data Engineer with 3+ years of production data engineering experience. You will maintain and enhance Python/SQL pipelines orchestrated via Airflow or Azure Data Factory, implement quality and schema checks, and troubleshoot pipeline issues to ensure reliable data flows.

You will also monitor data freshness, perform incident triage, support Spark jobs, and contribute to documentation and runbooks for operational stability.