Junior Data And Automation Engineer at Advansys – Cairo | Jobyssey

The candidate will be expected to support a pre-existing technical environment that may include:

A search and analytics platform in the ELK / OpenSearch family, fed by structured ingestion pipelines.
A low-code workflow automation layer used for business and operational workflows.
Internally developed Python services and AI-assisted agents integrated with third-party SaaS APIs.

Monitor the health of ingestion pipelines from relational data sources into the search platform; identify backpressure, stalled jobs, and mapping or schema issues.
Reprocess failed batches and support recovery from dead-letter queues.
Assist with index lifecycle management, templates, and routine cluster-health checks.
Support incremental changes to dashboards and visualizations, including new fields, metric fixes, and cosmetic updates.
Raise capacity and performance concerns to the senior engineer before they become incidents.

Monitor scheduled and event-driven workflow executions; triage failures and retry as appropriate.
Apply small to medium changes, including updated templates, new data fields, credential refreshes, and webhook endpoint rotations.
Maintain workflow stability when upstream third-party APIs change behavior.
Track queue depth, rate-limit handling, and idempotent retry behavior.

Monitor execution logs, error rates, and usage metrics of internal Python services and AI-assisted workflows.
Troubleshoot data ingestion into internal knowledge and retrieval systems.
Apply tested configuration or prompt adjustments under senior review.
Track and report on operational cost anomalies, including API usage and compute.

Write and maintain runbooks and known-good recovery procedures for every recurring incident class.
Keep a clean changelog of production changes via pull requests where applicable.
Participate in change windows and post-incident reviews.
Escalate complex issues with clear, structured diagnostic context, including logs, timestamps, and reproduction steps.

Working experience with the ELK / OpenSearch family, such as Elasticsearch, OpenSearch, Kibana, OpenSearch Dashboards, Logstash, or equivalent, with at least one of them used in a real environment.
Working experience with at least one low-code or workflow automation platform, such as n8n, Make, Zapier, Node-RED, Apache Airflow, or equivalent.
Comfortable with a relational database, such as PostgreSQL, MySQL, or similar: able to read schemas, write SELECT queries, and investigate data issues.
Comfortable on Linux: shell, SSH, log inspection, and process/service management.
Docker basics: running containers, reading compose files, and inspecting logs.
Reads and writes JSON and YAML fluently.
Able to read and modify existing Python and JavaScript / Node.js code. The candidate is not expected to design services from scratch.
REST API fluency: curl or Postman, headers, bearer tokens, pagination, rate-limit semantics, and webhooks.
Git fundamentals: branches, commits, pull requests, and resolving simple merge conflicts.
Structured troubleshooting: isolates variables, reads stack traces, bisects failures, and documents findings.
Exposure to any commercial LLM API or vector database.
Familiarity with common business SaaS APIs, including CRM, email, messaging, or data-enrichment platforms.
Prior exposure to a data-engineering, operations, analytics, or security domain.

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