Salesforce Data Engineer
New
J
JobgetherIT / Security
Based in India, Ability to align working hours with UK business hours.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLHadoopMicrosoft Power BIOracle ERPSalesforceAzureData engineeringSpark
Requirements
- Bachelor’s degree in Computer Science, Information Systems, Data Engineering, Software Engineering, Applied Mathematics, or a related technical discipline; Master’s preferred.
- 5+ years of progressive experience in data engineering, data integration, or backend data platform development.
- Demonstrated experience designing, building, and maintaining complex data pipelines across CRM, ERP, and cloud systems.
- Extensive hands-on experience with Salesforce, Siebel CRM, and Oracle ERP, including Data Loader, Workbench, and Inspector.
- Strong proficiency in SQL.
- Experience with large-scale data processing technologies such as Hadoop and Spark.
- Practical experience with Python or comparable programming and data-engineering technologies.
- Experience with BI and analytics platforms, particularly Power BI.
- Experience implementing data quality, validation, governance, and compliance frameworks.
- Ability to align working hours with UK business hours.
Responsibilities
- Design, build, deploy, and maintain scalable data workflows across Salesforce, Siebel CRM, Oracle ERP, SQL Server, and Azure Cloud environments.
- Conduct data profiling across revenue operations and go-to-market systems to assess data quality, consistency, completeness, and reliability.
- Engineer and enhance robust data pipelines and automated transformation processes using SQL and technologies such as Python, Hadoop, and Spark.
- Develop database scripts, triggers, functions, stored procedures, and other components required to support reliable enterprise data workflows.
- Create automated testing and validation processes that strengthen data quality, compliance, and governance.
- Partner with cross-functional teams to understand business and technical data requirements.
- Collaborate with technical teams, operations, product managers, and business stakeholders.
- Identify opportunities to automate data quality improvement processes.
- Support the preparation of datasets for machine learning and AI applications.
- Promote data engineering best practices and technical standards.
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