Data Architect job opportunity at Expereo.

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Date2026-02-02T12:05:50.558Z bot
Expereo Data Architect
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Pattern: Full-time
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degreeHigh School (S.S.C.E)
loacation Katowice, Poland
loacation Katowice....Poland

We are the  Intelligent Internet Platform.  We connect  People, Places  and  Things  anywhere,  managing Internet Performance  better than anyone else, while providing  One Global Experience,  giving  Visibility, Control  and  Security  through  expereoOne. Expereo believes in the power of Internet connectivity. As the world's largest provider of managed internet, SD-WAN/SASE, and Cloud connectivity solutions, we power enterprises and government sites worldwide, helping to enhance every business' productivity with flexible and optimal Internet performance. As a trusted partner of Fortune 500 enterprises and carriers, our continued aim and success in helping both our customers and partners depends solely on the talented individuals that make Expereo a dynamic, effective, multicultural, and equitable environment.   About the role We are looking for an experienced and versatile Data Architect to lead the end-to-end architecture of Expereo’s next-generation data and AI platform, enabling a unified customer and supplier experience and powering our evolution toward a full Network-as-a-Service ecosystem. This role covers the whole technical spectrum, from data ingestion and lakehouse design to governance, quality, automation, and AI enablement. It requires someone hands-on, fluent in modern data engineering practices, and capable of shaping the foundations that support synchronous, real-time, automated digital journeys for customers and suppliers.  The architect owns platform-wide decisions across ingestion frameworks, data modelling, reference architecture, orchestration, observability, machine learning integration, semantic modelling, catalogue strategy, and AI-assisted workflows. They design and implement a code-first, scalable, automated platform that unifies data from global suppliers, networks, and customer interactions, enabling intelligent UX, operational automation, and real-time insights.  This role is not a BI analyst or dashboard builder. It is an engineering-heavy, architecture-led position responsible for defining and building the technical backbone of Expereo’s data-driven future. You will work directly with the cloud architect, the network architect, and the lead architect, reporting directly to the Chief Innovation Officer.    Architectural Ownership  Define, own, and evolve Expereo’s end-to-end data platform architecture, including ingestion, lakehouse/warehouse, transformation, orchestration, governance, observability, and AI enablement.  Establish and maintain a unified data model that standardises inputs across the ecosystem, orchestration, and customer experience layers of the NaaS platform.  Lead design decisions for storage technologies, table formats, transformation frameworks, and streaming/batch patterns.  Data Engineering & Pipeline Design  Architect and implement scalable ELT/ETL pipelines across Salesforce, backend systems, CMDB, network telemetry, and supplier systems.  Define standards for raw, cleaned, and curated zones, ensuring quality, lineage, and reprocessability.  Introduce CDC, schema evolution, and automated ingestion patterns.  AI, ML & Intelligent Automation  Define the AI framework layered on top of the data platform, including feature stores, model lifecycle, data products, and ML observability.  Enable AI-driven actions across the digital customer journey, including pricing intelligence, delivery prediction, automated triggers, and proactive insights.  Build the foundations for conversational and semantic data discovery to support intelligent workflows and AI agents.  Governance, Quality & Metadata  Set up a scalable data governance architecture, including catalogue, lineage, data ownership, and sensitivity classification.  Implement automated data quality testing and observability integrated into pipeline orchestration.  Platform Integration & Scalability  Ensure the data foundation supports synchronous customer/supplier workflows, automated ordering, delivery tracking, support workflows, and NaaS service orchestration.  Ensure the platform is cloud-native, modular, and ready for integration into internal and external ecosystems.  Leadership & Collaboration  Act as the technical authority for data architecture decisions across engineering, product, commercial, and operations.  Guide engineers, ML engineers, and analysts through technical direction and best practices.  Partner with platform, supplier automation, and product teams to ensure data becomes a first-class citizen across the business. Technical Expertise  Deep hands-on experience building modern data platforms on cloud infrastructure (AWS preferred).  Strong expertise with lakehouse or warehouse architectures (Redshift, Snowflake, Databricks, all in Iceberg/Delta/Hudi formats).  Proven experience with ELT/ETL tooling such as Airbyte, dbt, Spark, Python pipelines, and orchestration frameworks (Dagster, Airflow, Prefect).  Strong understanding of structured, semi-structured, and streaming data processing.  Experience designing and deploying MLOps/AI architectures, including feature stores, model registries, monitoring, and inference pipelines.  Solid understanding of data governance, discovery, cataloguing, lineage, and metadata systems (DataHub or similar).  Deep knowledge of SQL, Python, and other relevant (scripting and programming) languages  Completely at ease and comfortable with DevOps, Git and everything-as-code practices  Architecture & System Design  Proven ability to design scalable, modular, cloud-native architectures supporting real-time workflows and cross-system integration.  Experience with domain modelling, event-driven patterns, and standardising data across heterogeneous systems.  Ability to design architectures supporting customer and supplier-level automation, digital ordering, and real-time visibility workflows.  Leadership & Delivery  Demonstrated capability to drive architectural decisions, challenge assumptions, and coordinate engineering execution.  Ability to translate high-level platform ambitions into technical blueprints and iterative implementation plans.  Strong communication skills; able to collaborate across engineering, product, operations, and commercial teams.  Mindset  Builder mentality with strong ownership.  Code-first, automation-first approach.  Comfortable designing data infrastructure for AI-native products and automated digital journeys.  Not a BI report writer. Not a dashboard specialist. Not a Powerpoint architect. A hands-on architect. 

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