Data Engineer

Key Responsibilities:

• Design, develop, and maintain 

  ETL pipelines

  using 

  Snowflake

  and related data transformation tools.
• Build and automate  

  data integration workflows

  that extract, transform, and load data from various sources including Oracle EBS and other enterprise systems.
• Analyze, monitor, and troubleshoot 

  data quality and integrity issues

  using standardized tools and methods.
• Develop and maintain 

  dashboards and reports

  using 

  OBIEE

  , 

  Power BI

  , and other visualization tools for business stakeholders.
• Work with IT and Business teams to gather reporting requirements and translate them into scalable technical solutions.
• Participate in 

  data modeling

  and storage architecture using star and snowflake schema designs.
• Contribute to the implementation of 

  data governance

  , 

  metadata management

  , and 

  access control mechanisms

  .
• Maintain 

  documentation

  for solutions and participate in 

  testing and validation

  activities.
• Support migration and replication of data using tools such as 

  Qlik Replicate

  and contribute to  

  cloud-based data architecture

  .
• Apply agile and DevOps methodologies to continuously improve data delivery and quality assurance processes.

Must have skills:

• 57 years

  of experience in 

  data engineering

  or 

  software development

  , preferably within a finance or enterprise IT environment.
• Proficient in 

  ETL tools

  ,  

  SQL

  , and 

  data warehouse development

  .
• Proficient in 

  Snowflake

  , 

  Power BI

  , and 

  OBIEE

  reporting platforms. Must have worked in implementation using these tools and technologies.
• Strong understanding of 

  data warehousing principles

  , including 

  schema design

  (star/snowflake), ER modeling, and relational databases.
• Working knowledge of 

  Oracle databases

  and Oracle EBS structures.

Preferred Skills:

• Experience with 

  Qlik Replicate

  , 

  data replication

  , or data migration tools.
• Familiarity with 

  data governance

  , 

  data quality frameworks

  , and 

  metadata management

  .
• Exposure to 

  cloud-based

  architectures, Big Data platforms (e.g., Spark, Hive, Kafka), and distributed storage systems (e.g., HBase, MongoDB).
• Understanding of 

  agile methodologies

  (Scrum, Kanban) and 

  DevOps

  practices for continuous delivery and improvement.

Why Join Cummins?

• Opportunity to work with a 

  global leader

  in power solutions and digital transformation.
• Be part of a 

  collaborative and inclusive

  team culture.
• Access to 

  cutting-edge data platforms

  and tools.
• Exposure to 

  enterprise-scale

  data challenges and 

  finance domain expertise

  .
• Drive impact through 

  data innovation and process improvement

  .