EazyStock is a “startup” within Syncron , a global market leading company developing solutions for after market service optimization. Whilst Syncron’s client base is made of up of large enterprises (e.g. Manitowoc, Mazda, DTNA and JCB), EazyStock work with small to medium sized companies, helping them to optimize their inventories and achieve high levels of customer satisfaction without having excess capital tied up in redundant inventory.
We at EazyStock are an entrepreneurial and international team operating in Europe, Asia and the USA, developing and selling the best cloud-based inventory optimization software solution on the market. Nevertheless, we know that we can only help SMB’s if they know about EazyStock, and how this best-in-class solution will improve their customer satisfaction, increase their profits and reduce their inventory investment!
About the role
We are looking for a Senior Data & AI Engineer to establish the data foundation of EazyStock — an engineer who owns the whole path from raw operational data to a model or an ML&LLM-powered feature in production.
Today our product data lives in operational databases and is consumed directly by analytics and early AI/ML use cases. Your mission is to change that: to design and build a lakehouse on Apache Iceberg, AWS Glue and Amazon Athena that provides a governed, read-only replica of product data, dramatically better analytics capabilities, and high-quality, contract-backed data feeds into Qlik and our AI/ML and GenAI workloads.
What would you do?
Lakehouse & Data Platform
Design and build the EazyStock lakehouse on S3 with Apache Iceberg as the table format, AWS Glue for ETL/ELT and cataloging, and Amazon Athena as the query engine.
Build reliable data integration and ingestion from operational sources.
Deliver a governed analytical replica that removes reporting load from production systems.
Design partitioning, compaction, snapshot and retention strategies; own query performance and cloud cost efficiency.
Data Modelling, Contracts & Governance
Deliver clean, well-modelled data into Qlik Cloud and into AI/ML consumers.
Define and operate data quality and data governance standards, with automated validation and meaningful alerting.
Introduce data contracts, schema evolution discipline and DataOps practices so producers and consumers can evolve independently.
Establish metadata management, data catalogs and lineage as a first-class part of the platform.
DataOps & Automation
Build CI/CD for data pipelines, infrastructure and configuration using GitHub, Jenkins and AWS DevOps tooling; treat infrastructure as code.
Implement observability for pipelines: metrics, logging, failure modes, SLAs and runbooks.
Design for repeatability and rollback — no manual production surgery.
AI/ML & MLOps
Build and operate MLOps pipelines on AWS — model packaging, deployment, versioning, monitoring and retraining.
Support our GenAI initiatives: data curation and indexing for RAG, embedding pipelines, and Amazon OpenSearch as a vector store, with models served through Amazon Bedrock.
Bring engineering discipline to AI work — evaluation, observability, cost control and security — so that AI features are supportable, not experiments in disguise.
Who you are? / What we expect:
6+ years of professional experience spanning data engineering and software engineering, with production systems you owned end to end and still stand behind.
Strong experience designing ETL/ELT pipelines, data models, and data integrations across heterogeneous sources.
Strong data modelling skills, and real experience with metadata management, data catalogs and lineage.
Practical experience with data quality and data governance.
Experience with data contracts, DataOps practices and schema evolution in a multi-consumer environment.
Confident with DevOps and CI/CD: Jenkins or equivalent, infrastructure as code, containerization (Kubernetes).
Deep, practical experience with the AWS data stack — S3, AWS Glue, Amazon…