TEAM UP RECRUITMENT SPÓŁKA Z OGRANICZONĄ ODPOWIEDZIALNOŚCIĄ
6. 3. 2026
Informacje o stanowisku
technologies-expected :
Data architecture
cloud computing
ETL/ELT
AI
technologies-optional :
AWS
Snowflake Data Cloud
Redshift
Databricks
about-project :
We are seeking a hands-on Data Architect to define and build a next-generation data and AI platform that enables real-time, automated digital journeys across customers and partners. This role owns the end-to-end data architecture, from ingestion and lakehouse design to governance, quality, and AI enablement.
This is an engineering-led architecture role, focused on building scalable, cloud-native data foundations rather than BI or reporting solutions.
Location: Katowice (1 day per week in the office, 4 days remote)
responsibilities :
Define and evolve the end-to-end data platform architecture, including ingestion, lakehouse/warehouse, transformation, orchestration, governance, observability, and AI enablement.
Establish a unified, standardised data model supporting operational, customer, and supplier workflows.
Lead architectural decisions around storage, table formats, batch and streaming patterns.
Design and implement scalable ELT/ETL pipelines across enterprise systems, network telemetry, and external partners.
Define standards for raw, refined, and curated data layers, ensuring data quality, lineage, and reprocessability.
Introduce CDC, schema evolution, and automated ingestion patterns.
Architect the data foundations for AI and ML, including feature stores, model lifecycle, and ML observability.
Enable AI-driven use cases such as pricing intelligence, prediction, automation triggers, and real-time insights.
Support semantic and conversational data access for intelligent workflows.
Design scalable data governance and metadata architectures, including catalogues, lineage, and ownership.
Implement automated data quality checks and observability embedded in pipelines.
Act as the technical authority for data architecture decisions across teams.
Guide data engineers, ML engineers, and analysts through standards and best practices.
Partner with platform, product, and operations teams to make data a first-class capability.
requirements-expected :
Strong hands-on experience building modern cloud-native data platforms (AWS preferred).
Expertise in lakehouse or warehouse architectures (e.g. Snowflake, Redshift, Databricks) and modern table formats.
Experience with ELT/ETL tooling, orchestration frameworks, and streaming data.
Solid understanding of MLOps and AI data architectures.
Strong SQL and Python skills; comfortable with DevOps and everything-as-code practices.
Proven ability to design scalable, modular architectures supporting real-time workflows and automation.
Experience standardising data across heterogeneous systems and event-driven environments.
Strong communication skills with the ability to influence technical and non-technical stakeholders.
Hands-on builder with strong ownership.
Automation-first, code-first approach.
Comfortable designing data platforms for AI-native and real-time products.
Not a BI or dashboard-focused role.
offered :
International, collaborative work environment.
Exposure to large-scale cloud, data, and AI platforms.