Senior Data Engineer (with Snowflake) @ Transition Technologies PSC
(práce v Polsku)

Senior Data Engineer (with Snowflake) @ Transition Technologies PSC

Polsko

• Experience: A minimum of 5-7+ years in data engineering, preferably supporting AI/ML applications and hold B.Sc., B.Eng., or higher, or equivalent in Computer Science, Data Engineering or related fields
• Experience with Snowflake
• Programming: Proficiency in Python, SQL and vector database native languages
• Databases: Experience with relational databases
• Cloud Platforms: Hands-on experience with AWS (OpenSearch, S3, Lambda) or Azure (Azure AI Search, Azure Blob Storage, Azure Automation)
• ETL/ELT Pipelines: Experience building scalable ETL/ELT workflows using dbt, Apache Airflow, or similar
• APIs & Microservices: Ability to design and integrate RESTful APIs for data exchange
• Data Security & Governance: Understanding of encryption and role-based access controls
• Version Control & DevOps: Familiarity with Git, CI/CD, containerization (Docker, Kubernetes), and Infrastructure as Code (Terraform, CloudFormation)
• Generative AI Support: Experience working with AI-specific data needs, such as embeddings, RAG (Retrieval Augmented Generation), and LLM fine-tuning data preparation

Nice to have:

• NoSQL, vector databases
• IoT data streaming (Kafka, Kinesis, PySpark etc).

,[Design, build, and maintain scalable data pipelines (ETL/ELT) leveraging Snowflake and Airflow, Implement optimized schemas, partitioning, and indexing strategies in Snowflake and relational databases, Develop data processing workflows and automation scripts in Python and SQL; integrate with APIs and microservices, Ensure scalability, performance, and resilience of pipelines; implement observability for jobs and data flows, Partner with data scientists and ML engineers to deliver high-quality datasets optimized for AI/ML workloads, Prepare, transform, and manage datasets for embeddings, RAG workflows, and LLM fine-tuning] Requirements: Snowflake, Python, AWS, ETL/ELT, CI/CD, Docker, Kubernetes, SQL, NoSQL, Vector Databases, Kafka, Kinesis, PySpark Additionally: Sport subscription, Training budget, Private healthcare, Flat structure, International projects, Free coffee, Playroom, Bike parking, Free snacks, Free beverages, In-house trainings, Modern office, No dress code.

Kategorie

data

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