Sr.Data Engineer

·       Development, maintenance, and enhancement of Data Pipelines (ETL/ELT) and processes with thorough knowledge of star/snowflake schemas

·       Developing complex SQL queries and SQL optimization

·       Hands on experience in PowerCenter/IDMC/IICS.

·       Development experience must be full Life Cycle experience including business requirements gathering, data sourcing, testing/data reconciliation, and deployment within Business Intelligence/Data Warehousing Architecture.

·       Designing and implementing data security

·       Monitoring and optimizing data storage and data processing

·       Use of Object function/object-oriented scripting languages including Scala, C++, Java and Python

·       Cloud data warehouse and data lake platforms (e.g., Snowflake, Databricks, Redshift, BigQuery, MS Synapse, MS ADLS, Apache Haddop, etc.)

·       Developing data services that are fit for purpose, resilient, scalable, and future proof, to meet user needs

·       Advanced SQL knowledge and experience working with relational databases and working familiarity with various databases.

·       Demonstrated understanding and experience using software and tools including ETL, relational SQL and NoSQL databases, big data tools like Kafka, Spark, and Hadoop

·       Experience building and optimizing ‘big data’ data pipelines, architecture, and data sets.

·       Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.

·       Strong analytic skills related to working with unstructured datasets.

·       Experience and knowledge of project management best practices and agile software development methodologies

·       A successful history of manipulating, processing, and extracting value from large, disconnected datasets.

·       Working knowledge of message queuing, stream processing, and highly scalable ‘big data’ data stores.

·       Strong project management and organizational skills.

·       Experience supporting and working with cross-functional teams in a dynamic environment.

·       Understanding of data science concepts

·       Familiarity with AI/ML frameworks and libraries

• Procurement / Supplier data
  
• Inventory and warehouse management data
  
• Demand planning and forecasting
  
• Production orders, BOMs, routings
  
• Shop floor / MES data
  
• Quality systems (defects, CAPA, nonconformance)