Syncron

Senior Data Engineer

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SeniorHybryda·WarszawaFull-time

#466053·Dodano 13 dni temu·0

Źródło: Syncron

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Tech Stack / Keywords

Apache IcebergAWS GlueAmazon AthenaS3Apache Spark Python Java SQL PostgreSQL Kubernetes

Podsumowanie oferty

We are looking for a Senior Data & AI Engineer to establish EazyStock’s data foundation, owning the path from raw operational data to models and AI-powered features in production. You will build a lakehouse using Apache Iceberg, AWS Glue and Amazon Athena, delivering governed product data and feeds to Qlik and AI/ML workloads.

Wymagania

6+ years of professional experience spanning data engineering and software engineering, with production systems owned end to end.
Strong experience designing ETL/ELT pipelines, data models and integrations across heterogeneous sources.
Strong data modelling skills and practical 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.
Confidence with DevOps and CI/CD, including Jenkins or equivalent, infrastructure as code and containerization (Kubernetes).
Deep, practical experience with the AWS data stack, including S3, AWS Glue, Amazon Athena, IAM and Lambda, and with operating workloads in the cloud.
Hands-on experience with Apache Spark and Apache Iceberg or a comparable open table format such as Delta Lake or Hudi, including compaction, partition evolution and snapshot management.
Strong SQL and PostgreSQL skills, including query optimization, indexing, execution plans, replication and CDC.
Strong Python and solid production Java skills, including the ability to own a backend service.
Experience integrating with Qlik or another enterprise BI platform.
A Data Engineering Certificate, such as Google Professional Data Engineer, Microsoft Certified Fabric Data Engineer, AWS Certified Data Engineer, Databricks Certified Data Engineer, Snowflake SnowPro Advanced Data Engineer, or equivalent.
Proven ability to mentor and upskill engineers who are strong developers but new to data.

Nice to have:

Familiarity with classical ML tools such as scikit-learn, XGBoost and 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, including RAG, embeddings, vector databases or LLM APIs, in a production or serious prototype context.
Practical MLOps experience deploying models to production, versioning, monitoring and retraining, and willingness to own the model lifecycle.
Domain experience in Supply Chain, Inventory, Pricing or Service Fulfilment.

Obowiązki

Lakehouse & Data Platform:

Design and build the EazyStock lakehouse on S3, using 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 AI/ML consumers.
Define and operate data quality and 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 pipeline observability, including metrics, logging, failure modes, SLAs and runbooks.
Design for repeatability and rollback, avoiding manual production surgery.

AI/ML & MLOps:

Build and operate MLOps pipelines on AWS, covering model packaging, deployment, versioning, monitoring and retraining.
Support GenAI initiatives with data curation and indexing for RAG, embedding pipelines, Amazon OpenSearch as a vector store, and models served through Amazon Bedrock.
Bring engineering discipline to AI work through evaluation, observability, cost control and security so AI features are supportable.

Benefity

Opportunities to lead strategic initiatives, influence product direction and expand expertise in AI and SaaS.

Syncron

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