Data Architect

ITFS is a consulting company from the Tricity area with a branch in Warsaw. It focuses primarily on IT specialist recruitment and creating project-based Software Houses for clients from Poland and Western Europe.

• Several years of experience in data architecture and engineering in complex, multi-source environments.
• Hands-on approach combining architecture design with practical implementation.
• Strong knowledge of Python, SQL, and modern platforms such as Databricks, Snowflake, Fabric, Synapse, BigQuery, Redshift.
• Experience with cloud platforms Azure, AWS, or GCP.
• Practical knowledge of Lakehouse, Data Lake, Data Warehouse architectures and data modeling.
• Design of batch, streaming, and event-driven architectures including real-time processing.
• Familiarity with streaming technologies like Kafka, Spark Streaming, Flink, Event Hubs, Kinesis.
• Experience building data pipelines and system integration including API, IoT, telemetry, corporate systems.
• Practical knowledge of CI/CD, DataOps, IaC and tools like Git, Terraform, dbt, Airflow, Azure DevOps.
• Experience in Data Governance, metadata, data lineage, and data quality management.

Nice to have:

• Knowledge of Scala.
• Experience in IoT, edge-to-cloud, digital twins, AI-ready platforms, or synthetic data.
• Experience in industrial, infrastructure sectors or regulated and operationally critical environments.

• Design and develop comprehensive data architecture from source systems through pipelines to storage, integration, API, and consumption layers.
• Build a modern Lakehouse platform supporting analytics, AI, operational data, telemetry, and data products.
• Implement scalable pipelines for structured, unstructured data, and batch, near real-time, and real-time processes.
• Integrate data from corporate systems, devices, sensors (IoT), operating systems, and external providers.
• Develop streaming and event-driven architectures for operational and telemetry data processing.
• Create data models including canonical models and integration and interoperability standards for business.
• Implement DataOps, CI/CD, engineering standards, monitoring, and testing to ensure platform reliability.
• Collaborate on Data Governance including security, access management, metadata, data lineage, and data quality.
• Lead greenfield projects, prototype development (PoC), and actively participate in technology and tool selection.
• Provide technical leadership by making architectural decisions and bridging communication between technical and business teams.