Senior Data Engineer

We are seeking an experienced Data Engineer to join our dynamic technology team. 

What is the role about?

This role is pivotal in developing and maintaining data pipelines from siloed sources such as Salesforce, Netsuite, Airtable, and custom applications in our Microsoft Azure/Microsoft Fabric environment. The Data Engineer will ensure data is processed and warehoused efficiently, making it accessible for analysis and visualization by the RevOps teams. This position also involves working closely with IT and software development teams to identify data source priorities and future strategies.

What is the project about?

The Client is from the USA. The project supports Finance and tax-related business logic and is successful in the US market. 

Team

We have a team of 5 skilled Senior Developers, a Project Manager, and an Account Manager. On the client side, you can expect communication with the Product Owner, CTO, and Data Engineer.

How many stages of the interview are there?

• Interview with the Recruiter — up to 30 min.
• Tech interview on our side — up to 1 hour
• Tech interview on a client-side  — up to 1 hour

Requirements:

• 4+ years of experience as a Data Engineer.
• Upper-Intermediate or higher proficiency in English.
• Proven experience in writing efficient code for data pipelines.
• Expertise in Microsoft Azure and Microsoft Fabric environments.
• Strong knowledge of SQL, Python, and data processing frameworks (such as Apache Spark, and Data Factory).
• Familiarity with query optimization techniques.
• Experience working with data sources like Salesforce, NetSuite, and Airtable.
• Skilled in extracting data from APIs and various data sources.
• Experience with data warehousing solutions and data modeling.

Responsibilities: 

• Own the end-to-end development and maintenance of data pipelines between disparate systems and the Microsoft stack.
• Integrate data from sources like Salesforce, Netsuite, Airtable, and custom apps into the Microsoft Azure/Microsoft Fabric environment.
• Collaborate with stakeholders to identify data source priorities and future strategies.
• Ensure data is processed in a manner that supports last-mile users in performing final analysis and visualization.
• Support DevOps teams in utilizing the system for accurate and timely analysis.