On July 1, 2025, IPL Plastics merged with Schoeller Allibert to form IPL Schoeller (the ‘company’). The Company’s operations are split across North America (11 manufacturing plants) and Europe (16 manufacturing plants).
The company is headquartered in Dublin, Ireland. IPL Schoeller is a leading manufacturer of sustainable rigid packaging solutions across a range of end market segments including returnable transit packaging, consumer and industrial packaging, environmental containers and agricultural packaging. The company employs c.4,100 employees across North America (1,600 employees) and Europe (2,500 employees.)
The role
As a Data Engineer, you will strengthen IPL Schoeller’s internal data engineering capability as the BI and data function continues to move from traditional report development towards governed enterprise data platforms. The role is focused on building reliable, scalable and well documented data assets that support Microsoft Fabric adoption, enterprise analytics, operational reporting and long-term reduction in dependency on external contractor delivery.
Act as a hands-on mid-level data engineer within the BI and Data team, with primary focus on data pipelines, SQL transformation logic, data modelling and platform reliability.
Design, build and maintain batch and near real-time data pipelines using Microsoft Fabric, Synapse, Azure Data Factory or similar Microsoft data platform technologies.
Develop, optimise and support SQL based transformations, ingestion routines, stored procedures and curated data structures across structured and semi-structured data sources.
Build and maintain lakehouse, warehouse and staged data architectures that support reusable enterprise datasets, semantic models, dashboards and downstream operational use cases.
Apply sound data modelling practices, including dimensional modelling, source-to-target mapping and calculation logic aligned to reporting and analytical requirements.
Integrate data from ERP, finance, operational systems, APIs, files and external platforms into governed enterprise data assets.
Implement data quality checks, reconciliation controls and monitoring routines to improve accuracy, trust and repeatability across data pipelines and reporting outputs.
Monitor pipeline runs, investigate failures and troubleshoot performance issues across ingestion, transformation, storage and refresh layers.
Support optimisation of query performance, data refresh strategies, partitioning and processing efficiency as the Microsoft Fabric platform becomes embedded across the business.
Working with the BI Manager, assist in the development of semantic models and BI report builds as required, while recognising that BI report development is a supporting activity rather than the primary function of the role.
Contribute to development standards, documentation, release management, version control and structured ways of working across the BI and Data team
Support security, governance and access controls across the data platform in line with internal IT and data governance standards.
Participate in continuous improvement initiatives to reduce manual data handling, improve platform resilience and support a controlled transition towards stronger internal data engineering capability
Bachelor’s degree in Computer Science, Information Systems, Engineering, Mathematics, or a related discipline
Equivalent experience in data engineering, ETL/ELT development, or cloud data platform delivery will be highly advantageous
Master’s degree desirable.
5+ years of experience in data engineering, database development, analytics engineering, or similar data platform roles.
Hands-on experience with SQL and building data pipelines in Microsoft-centric platforms, including Microsoft Fabric, Azure Synapse, Azure Data Factory, or other related technologies.
Experience working with data modelling, data warehousing, and integrating multiple source systems.
Experience working with Power BI or Tableau for downstream analytics and reporting consumption.
Experience working across cross-functional teams in a global or matrix organisation is advantageous
