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- Hybrid Working Model: 2 days in office Duration: 12+ months contract highly extendable The Role Involved in the design, build and integration of data from various sources and the management of the existing Data Ingestion solutions in SQL Server and Azure environments.
Also managing the delivery of multiple projects related to the Microsoft Distribution Data Ingestion (SSIS and Azure) on a day-to-day basis, within agreed constraints and the specified tolerances of scope, time, cost and quality.
Your responsibilities: Understanding business requirements relating to business data sources with the ability to integrate it and/ or transform it effectively in accordance with business needs The customisation and development of Extract Transform and Load routines, modelling, queries, reports and analysis Support of existing data marts, warehouse process chains and associated models and queries, Facilitating the follow up meetings and removing obstacles that affect the team Ensuring the successful delivery of all assigned Data Engineering work on projects.
across all business units and their successful deployment to production, including use cases developed by our Data Science & Analytics team, Implement changes (CRs) or new developments relating to existing data warehouses Tracking project status and managing the project teams to mitigate issues and risks Troubleshooting and resolving issues with any of our SQL Server and Azure data repositories Your Profile Essential skills/knowledge/experience: 10+ years of relevant experience in Azure Data Engineering covering ADF, Synapse, and MS Fabric Significant experience in Software Development and Agile Methodologies.
Excellent experience in developing ETL routines, processes and structures.
Expert in Power Automate, Azure Data Factory, Azure Synapse, MS Fabric ETL development.
Significant experience in developing Python or PySpark.
Proven ability to set up Azure Synapse and MS Fabric connectors (On-prem file shares, SFTP, SQL database, HTTP, etc..) in real world enterprise environment.
Significant experience in Database and Data Storage Platforms including Azure Data Lake.
Excellent experience in developing ETL routines, processes and structures.
Extensive knowledge of data modelling techniques and structures.
Practical experience of developing ETLs for Dimensions and Fact Tables including SCD I and II implementation.
Excellent experience of deploying solutions in a fast-moving enterprise.
Ability to validate engineering data and deploy solutions.
Experience in incident, change management and transition processes Creating data catalogues Identifying Critical Data Elements Recording data quality business rules Carrying our data quality assessments Creating data quality assessment scripts & ETLs Creating data quality reports Designing & implementing Conceptual, Logical, and Physical data models Designing & implementing Relational data models with normalization Designing & implementing Dimensional Data Models (Dimensions, Facts, SCD I, SCD II, SCD III) Designing & implementing Lakehouse and Delta Lake architectures Reporting, documentation/writing.
Excellent Organisational skills.
Experience with a wide range of IT environments, platforms and applications IT Qualification
- Degree or equivalent work experience Professional certifications e.g.
MCSE Candidates should hold appropriate RTWs for Ireland i.e.
Stamp1G Spousal, Stamp 4, Irish/British/EU passport.
For more information please apply here or contactContact: Ref: EET8127 Candidates must be eligible to work full time and long term in the location specified or currently hold a valid appropriate long term work Visa to apply.
eirbusinesstalent, eir business and our clients are equal opportunity employers who seeks to recruit and appoint the best available person for a job regardless of marital / civil partnership status, sex (including pregnancy), age, religion, belief, race, nationality and ethnic or national origin, colour, sexual orientation or disability.
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Skills: Azure MS Fabric ETL Benefits: Work From Home
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Sprachkenntnisse
- English
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