Senior Data Engineer
Senior Data & AI Engineer at EazyStock (a startup within Syncron, which makes inventory/aftermarket service optimization software) builds the company's AWS lakehouse on S3, Apache Iceberg, Glue and Athena, delivers governed data feeds to Qlik and AI consumers, and runs MLOps and GenAI pipelines (RAG, Bedrock) using Python, Java, SQL and PostgreSQL.
- Compensation
- Not disclosed
- Location
- Poland
- Work setup
- Hybrid
- Seniority
- Senior
- Employment
- Contract
- Experience
- 6+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 6+ years of professional experience spanning data engineering and software engineering, owning production systems end to end
- Strong experience designing ETL/ELT pipelines, data models, and data integrations across heterogeneous sources
- Strong data modelling skills; 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, Kubernetes
- Deep practical experience with the AWS data stack: S3, AWS Glue, Amazon Athena, IAM, Lambda
- Hands-on experience with Apache Spark and Apache Iceberg (or Delta Lake / Hudi), including compaction, partition evolution, snapshot management
- Strong SQL and PostgreSQL: query optimization, indexing, execution plans, replication and CDC
- Strong Python and solid production Java
- Experience integrating with Qlik or another enterprise BI platform
- Data Engineering Certificate (Google Professional Data Engineer, AWS Certified Data Engineer, Databricks, Snowflake SnowPro, or equivalent)
- Proven ability to mentor and upskill engineers new to data
Nice to have
- Classical ML familiarity: scikit-learn, XGBoost, pandas
- Track record of leading a greenfield data platform build or significant cloud data migration
- Experience with Airflow, dbt or similar
- Experience with SageMaker, MLflow, Kubeflow or comparable model lifecycle tooling
- Working experience with LLM-based systems: RAG, embeddings, vector databases, LLM APIs
- Practical MLOps experience: deploying, versioning, monitoring and retraining models in production
- Domain experience in Supply Chain, Inventory, Pricing or Service Fulfillment
Full description
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.
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.