
Data & AI Engineer
At a Glance
- Category
- 💻 Technology
- Level
- Mid-Level
- Type
- Full-time
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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 & AIAnalytics, 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.
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.
- Work in an international environment
- Innovation in FinTech
- Private Health Insurance
- Hybrid Working Model
- Training & Development
- Performance Bonus
Requirements
- •Bachelor’s or master’s degree in computer science, Statistics, Mathematics or related
- •Hands-on experience as a data engineer with focus on Snowflake, Databricks, Azure, GCP and/or AWS
- •Strong programming skills in Python and SQL
- •Proven experience in developing ETL/ELT pipelines using AWS Glue, dbt, Informatica, Talend, Python, SQL, etc.
- •Experience delivering CI/CD and DevOps capabilities in a data environment
- •Collaborative, proactive problem solver
Nice to Have
- •Working knowledge of SAP HANA / SAP HANA Cloud data modelling, XS Engine, Calculation Views, SDI/SDA
- •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
- •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
Responsibilities
- •Develop, test, and maintain robust ETL/ELT pipelines to ingest and transform data from multiple sources
- •Implement robust web scraping solutions
- •Optimize performance and cost of data workloads (partitioning, compression, query tuning, etc.)
- •Apply data modelling standards (dimensional models, star/snowflake schemas, canonical data models)
- •Contribute to the evolution of the cloud data architecture
- •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 and dashboards
- •Follow data governance, data quality, and metadata management practices
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