Data engineer in lakehouse

Syncron; Warszawa; Hybrid; PLN 180,000 - 320,000; Full time

Job summary

Syncron развивает EazyStock — стартап внутри компании, ориентированный на оптимизацию запасов и обслуживание клиентов по глобальному рынку. В команде вы будете строить единую аналитическую платформу, интеграцию данных и ML‑помощь для принятия решений.

Требуется 6+ лет опыта в data/software инженерии, глубокое владение SQL, Python и инструментами AWS. Готовность работать в гибридном формате и развивать DataOps, MLOps и качество данных.

Qualifications

• 6+ лет опыта в дата-инженерии и разработке ПО, end-to-end.
• Опыт проектирования ETL/ELT, моделей данных и интеграций.
• Эмпирический опыт управления данными, метаданные и картина lineage.
• Опыт работы с AWS стеком: S3, Glue, Athena, IAM, Lambda.

Responsibilities

• Разработка lakehouse на AWS с Iceberg как форматом таблиц и Glue для ETL/ELT.
• Обеспечение качественных и управляемых данных для аналитики и ML.
• Определение стратегий партиционирования, сжатия и контроля затрат.
• OCI изменений по данным, DataOps и CI/CD для пайплайнов.

Skills

SQL

Python

Java

Data Modeling

Data Governance

DevOps

CI/CD

Cloud

Education

Google Data Engineer

Azure Data Engineer

AWS Data Engineer

Databricks Data Engineer

Snowflake Data Engineer

Tools

Apache Spark

Apache Iceberg

Delta Lake

AWS Glue

S3

Amazon Athena

IAM

Lambda

Kubernetes

Airflow

dbt

SageMaker

Kubeflow

Job description

Описание

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.

Задачи

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. 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. 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. 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. mentor and upskill engineers who are strong developers but new to data.

Требования

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 Athena, IAM, Lambda — and with the operational side of running workloads in the cloud. Hands‑on experience with Apache Spark, Apache Iceberg (or a comparable open table format such as Delta Lake / Hudi) — including the operational realities: compaction, partition evolution, snapshot management. Strong SQL and strong PostgreSQL — query optimization, indexing, execution plans, replication and CDC. Strong Python and solid production Java — you can own a backend service, not just call one. Experience integrating with Qlik or another enterprise BI platform. Data Engineering Certificate: Google Professional Data Engineer, Microsoft Certified Fabric Data Engineer, AWS Certified Data Engineer, Databricks Certified Data Engineer, Snowflake SnowPro Advanced Data Engineer, or equivalent. Будет плюсом: Classical ML familiarity — scikit‑learn, XGBoost, pandas; A track record of leading a greenfield data platform build or a 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, or LLM APIs in a production or serious prototype context; Practical experience with MLOps: deploying models to production, versioning, monitoring and retraining — and a willingness to own the model lifecycle, not just the data that feeds it; Domain experience in Supply Chain, Inventory, Pricing or Service Fullfilment

Условия

Impactful Work – Your contributions will directly shape the future of EazyStock’s product and deliver measurable value to customers worldwide. Innovation‑Driven Culture – Join a team that thrives on creativity, collaboration, and cutting‑edge technology. Professional Growth – Opportunities to lead strategic initiatives, influence product direction, and expand your expertise in AI and SaaS. Global Environment – Work with colleagues and customers across multiple countries in a truly international setting. Flexibility – A hybrid work model that supports work‑life balance.