Senior Data Engineer

Company Description

evercare is a Saudi longevity and wellness company redefining modern healthcare through preventive, science-backed solutions focused on long-term health, performance, recovery, and quality of life. Through personalised care delivered by licensed doctors, nurses, and specialists, evercare helps individuals optimise their health proactively, not just treat symptoms after they appear.

Our mission is to add more life to years rather than just years to life, by helping people feel their best, perform and live better for longer.

Job Description

We are hiring a hands-on Senior Data Engineer

to lead the design and implementation of a modern, cloud-based data platform.

This role is approximately 80% data engineering and 20% business intelligence

. You will own the data lifecycle from source-system ingestion and warehouse architecture through transformation, customer identity resolution, monitoring, and management reporting.

This is an opportunity for a practical and independent engineer to build a reliable data foundation in an evolving, high-impact environment.

You will build and operate a cloud data platform using technologies such as:

• Google Cloud Platform
• BigQuery
• Python
• REST APIs and webhooks
• Managed data-ingestion connectors
• dbt
• Cloud-based workflow orchestration
• Containerized cloud workloads
• Cloud object storage
• Cloud-based business intelligence tools

The data environment includes CRM, ERP, customer messaging, telephony, and healthcare-management platforms.

Some source systems can be integrated through direct or managed connectors, while others will require custom API or webhook integrations.

Key Responsibilities

Data Engineering - %80

• Design and implement a scalable, secure, and maintainable BigQuery data warehouse.
• Build Python-based API and webhook integrations for platforms without direct connectors.
• Configure and manage ingestion for platforms supported by managed connectors.
• Develop incremental data pipelines with pagination, retries, rate-limit handling, logging, and failure recovery.
• Deploy and operate containerized ingestion workloads in a cloud environment.
• Schedule, orchestrate, and monitor data workflows.
• Build dbt models across raw, staging, core, identity, and reporting layers.
• Implement automated data-quality checks, testing, alerting, and pipeline monitoring.
• Establish secure practices for credentials, access control, and sensitive information.
• Maintain strong Git, deployment, and code-review practices.
• Document the platform architecture, pipelines, data models, and operating procedures.

Customer Identity Resolution

A major part of the role will involve building a trusted, unified customer view across multiple systems.

You will:

• Design and maintain a canonical customer identifier.
• Normalize phone numbers, email addresses, and source-system identifiers.
• Build cross-system customer mapping and record-linkage models.
• Implement deterministic matching rules and confidence-scoring methods.
• Identify and manage duplicate, incomplete, and conflicting records.
• Establish safeguards to prevent incorrect customer or patient record merges.
• Support the secure activation of curated data back into operational platforms.

Business Intelligence - %20

• Connect a cloud-based BI platform to curated BigQuery models.
• Build simple, practical dashboards for management and operational teams.
• Create reporting for sales, revenue, invoices, appointments, customer communications, and call activity.
• Partner with stakeholders to define, validate, and document business metrics.
• Add filters, drill-down capabilities, and appropriate access controls.
• Train and support business users in basic dashboard usage and self-service reporting.

Recuired Qualifications

• Typically 4+ years of professional experience in data engineering

  , or equivalent demonstrated experience delivering production-grade data platforms.

• Strong SQL and Python skills.

• Hands-on experience designing and developing data warehouses in BigQuery.

• Proven experience building REST API integrations and production data pipelines.

• Experience with dbt or a comparable data-transformation framework.

• Experience with workflow-orchestration tools such as Prefect, Airflow, or Dagster.

• Experience deploying and operating containerized workloads in a cloud environment.

• Strong understanding of data modeling, incremental processing, automated testing, and data-quality practices.

• Experience handling pipeline failures, API limitations, incomplete documentation, and inconsistent source data.

• Strong Git and technical-documentation practices.

• Ability to independently own an implementation from source-system ingestion through to business reporting.