Data & AI Engineer — Hybrid Role for Scalable Pipelines

ATOM Insurance; Abu Dhabi; Hybrid; AED 300,000 - 480,000; Full time

Benefits offered by this job

Hybrid Working Model

Private Health Insurance

Training & Development

Performance Bonus

Job summary

ATOM Insurance is seeking a Data & AI Engineer to drive end-to-end data development, ingestions, cleansing, validation and curation. You will work with a team to deliver secure data and AI solutions across the data lake, warehouses, and analytics platforms.

You will develop ETL/ELT pipelines, implement web scraping, and optimise data workloads. Collaboration across product, engineering and business units is essential to deliver scalable data architectures and AI-enabled insights.

Qualifications

• Hands-on experience as a data engineer with focus on Snowflake, Databricks, Azure, GCP and/or AWS platforms.
• Strong programming skills in Python, SQL and other relevant languages.
• Proven experience in developing ETL/ELT pipelines using various technologies including AWS Glue, dbt, Informatica, Talend, Python, SQL.
• Working knowledge of SAP HANA data modelling is a strong plus.
• Experience with analytical frontend tools (Power BI, Tableau, etc).
• Exposure to Large Language Models and AI workflows with orchestration tools.

Responsibilities

• Develop, test, and maintain robust ETL/ELT pipelines across data lake and data marts.
• Implement robust web scraping solutions.
• Collaborate with data scientists and analytics teams to productionise ML/AI solutions.

Skills

ETL/ELT pipelines

Python

SQL

Data engineering

CI/CD in data

Education

Bachelor's or Master's in CS/Statistics/Math or related

Tools

Snowflake

Databricks

Azure

GCP

AWS

dbt

Informatica

Talend

Job description

ATOM Insurance is seeking a Data & AI Engineer to drive end-to-end data development, ingestions, cleansing, validation and curation. You will work with a team to deliver secure data and AI solutions across the data lake, warehouses, and analytics platforms.

You will develop ETL/ELT pipelines, implement web scraping, and optimise data workloads. Collaboration across product, engineering and business units is essential to deliver scalable data architectures and AI-enabled insights.