ATOM Technologies Limited
Data & AI Engineer
full timeseniorDubai, UAERemote1 weeks ago
25,000-45,000 AED/mo
Data & AnalyticsJob Description
As a Data & AI Engineer you will contribute across the end-to-end data development lifecycle for data ingestion, cleansing, validation, transformation, curation and presentation. Reporting to the Principal Data & AI, you will work within a talented team of data engineers to deliver high-impact, secure data and AI solutions that support the ambitious goals of ATOM’s customers and stakeholders. Hands-on Engineering & Delivery - Develop, test, and maintain robust ETL/ELT pipelines to ingest and transform data from multiple sources into the data lake and data marts. - Implement robust web scraping solutions. - Optimise performance and cost of data workloads (partitioning, compression, query tuning, etc). Data Modelling & Platform Contribution - Apply data modelling standards (dimensional models, star/snowflake schemas, canonical data models) to support reporting, analytics, and AI/ML use cases. - Contribute to the evolution of the cloud data architecture under the guidance of the Principal Data & AI Analytics, BI, ML & AI Enablement - Work with data scientists, ML engineers, and analytics teams to support the productionisation of ML/AI and LLM-based solutions through reliable data pipelines and feature stores. - Expose curated, well-documented datasets to enable self-service reporting, dashboards, and analytical tools. Quality, Governance & Security - Follow data governance, data quality, and metadata management practices across the platform. - Apply security best practices (IAM roles and policies, encryption at rest and in transit, row/column-level security, auditing and monitoring). Collaboration & Ways of Working - Partner with product, engineering, and business stakeholders to understand data requirements and translate them into scalable technical solutions. - Contribute to code reviews, documentation, testing, and DevOps/CI-CD practices, adhering to clean code, reusable components, version control, and observability standards. Benefits - Work in an international environment - Innovation in FinTech - Private Health Insurance - Hybrid Working Model - Training & Development - Performance Bonus
Requirements
- KNOWLEDGE AND SKILLS
- - 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 but not limited to AWS Glue, dbt, Informatica, Talend, Python, SQL, etc.
- - Working knowledge of SAP HANA / SAP HANA Cloud data modelling, XS Engine, Calculation Views, SDI/SDA is a strong plus.
- - Experience with analytical frontend tools (e.g. MicroStrategy, Power BI, Tableau, Spotfire, SAP Analytics Cloud).
- - Exposure to Large Language Models, generative AI, and intelligent agents, including hands-on experience building Agentic AI workflows and automations using tools such as n8n, LangChain / LangGraph, Make (Integromat), Zapier AI, CrewAI, or similar orchestration frameworks.
- - Experience delivering CI/CD and DevOps capabilities in a data environment.
- - A bachelor’s or master’s degree in computer science, Statistics, Mathematics or related.
- - A collaborative, proactive problem solver with strong communication skills and the ability to work effectively across diverse teams.
- - At minimum 5 years of experience in a similar role.
Responsibilities
- Hands-on Engineering & Delivery
- - Develop, test, and maintain robust ETL/ELT pipelines to ingest and transform data from multiple sources into the data lake and data marts.
- - Implement robust web scraping solutions.
- - Optimise performance and cost of data workloads (partitioning, compression, query tuning, etc).
- Data Modelling & Platform Contribution
- - Apply data modelling standards (dimensional models, star/snowflake schemas, canonical data models) to support reporting, analytics, and AI/ML use cases.
- - Contribute to the evolution of the cloud data architecture under the guidance of the Principal Data & AI Analytics, BI, ML & AI Enablement
- Analytics, BI, ML & AI Enablement
- - Work with data scientists, ML engineers, and analytics teams to support the productionisation of ML/AI and LLM-based solutions through reliable data pipelines and feature stores.
- - Expose curated, well-documented datasets to enable self-service reporting, dashboards, and analytical tools.
- Quality, Governance & Security
- - Follow data governance, data quality, and metadata management practices across the platform.
- - Apply security best practices (IAM roles and policies, encryption at rest and in transit, row/column-level security, auditing and monitoring).
- Collaboration & Ways of Working
- - Partner with product, engineering, and business stakeholders to understand data requirements and translate them into scalable technical solutions.
- - Contribute to code reviews, documentation, testing, and DevOps/CI-CD practices, adhering to clean code, reusable components, version control, and observability standards.
Skills Required
SnowflakeDatabricksAzureAWSGCPPythonSQLETL/ELT pipelinesdbtInformaticaTalendSAP HANAPower BITableauMicroStrategySAP Analytics Cloudn8nLangChainMake (Integromat)Zapier AICI/CDDevOps
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