Data & AI Engineer

Job 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.
Work in an international environment Innovation in FinTech Private Health Insurance Hybrid Working Model Training & Development Performance Bonus 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.