We are looking for a IT Data Engineer Manufacturing

Responsibilities:
• You will develop and support complex technical solutions focusing on ingestion and processing data to PMI Data platform.
• You will follow and contribute to PMI development standards and practices to enable advanced analytics capabilities.
• You will use existing technology and tools to develop and support data pipelines.
• You will handle production support issues as they arise.
• You will be part of cross-use cases team (Technical Capability) which delivers Data Products or Digital Analytics use cases.
• You will create guidelines and best practices in data engineering area. You will contribute in creation of internal frameworks, common features to improve speed of delivery of Digital Analytics Products.
• You will collaborate with PMI Architects, Data Architects, DevOps Engineers and Data Scientists to develop new solutions and search for new technologies.
• You will communicate with various stakeholders with different technical background, business, management.
• You will contribute to platform architecture, CICD processes in data engineering area.

Experience/ Specific Skills:
• At least 3+ year experience in creating data pipeline
• SQL programing (advanced level)
• Knowledge in data modeling (multidimensional data models)
• Experience in building ETL processes
• Alternatively: Basic knowledge of AWS services for data processing
• Alternatively: Good knowledge of Scala or Java or Python
Proficient with Data Engineering Best Practices:
 Agile Methods
 Continuous Integration & Development
 Software Development (Code & Architecture Review, Pair Programming)
 Data Modelling and DataWarehouse theory (e.g. Data Vault, Star schema, SnowFlake schema)
Mastering with ETL/ELT Technical Stacks and programming languages:
• Microsoft (SSIS, AWS Glue)
• SQL, Python
Confident in RDBMS and Big-Data technical stack
• AWS Cloud Services Computing (S3, RDS, RedShift)
• SQL database (SQL Server, PostGres or equivalent)
• No SQL Databases
 TimeSeries Databased (Influxdata stack)
 Document DB (MongoDB-like)
 Key-Store DB (Redis-like)
Proficient with Architectural Patterns and Practices:
• Identity and Access Management Concepts (SSO, RBAC, OAuth)
• Telemetry and Observability (Tracing, Metrics, etc.)
Confident in working with Databases and Data Architectures
• SQL database (SQL Server, PostGres or equivalent)
• No SQL Databases
 TimeSeries Databased (Influxdata stack)
 Document DB (MongoDB-like)
 Key-Store DB (Redis-like)
Containers and Orchestrator Kubernetes concept (POD)