Data Engineer

Data Engineer

Dynamic work schedule - This is 5 days on site a month- in the same week then the remainder of the month is working from home. (They can fly/drive into the office as well). If your candidate is not open to this please tell them they will not be considered. Fidelity determines the week they are working onsite, not the candidate.

Location: Any

Top Skills: Informatica to Nifi (Understanding of workflow, Data migrations

We are searching for a hardworking Data Engineer to join our Enterprise Software Engineer to support our cloud goals for data infrastructure. Our group is dedicated to craft of engineering and a culture of continuous learning with a full dedicated day to upskill each week. We work as a team along with our partners in Corporate Technology Group and the Enterprise to deliver business value across many applications and functions. We are looking for open-minded individuals who take pride in delivering at a high velocity while also embracing a cloud-first attitude.

The Team

Fidelity believes that diversity in background and experience will foster an engaging work environment for their employees. They recognize the value that employees’ differences can contribute to the forward-thinking and strong future of the company. This ultimately enables Fidelity to deliver better outcomes and support for their associates and customers. As a Data Engineer, you will drive outstanding business initiatives that improve the long-term value of the Firm. People who are phenomenal cultural fits demonstrate curiosity to learn, step up and take the lead, take ownership of their projects, and thrive by sharing knowledge.

The Expertise You Have

• Experience with relational SQL and NoSQL databases, including Postgres and Oracle.
• Demonstratable expertise with data pipeline and workflow management tools: Informatica, Nifi, ControlM etc.
• Cloud abilities specifically in AWS EC2 and RDS.
• Experience in serverless technologies such as AWS Lambda a plus.
• Data DevOps experience, implementing and deploying data infrastructure as code.
• Familiarity with source code management systems for DML/DDL versioning.
• Collaboration with data and analytics collaborators.
• Understanding of software development lifecycle.
• Strong experience working in agile environment.