Data Engineer - job 1 of 2

As a Data Engineer at Accenture Federal Services, your main responsibilities will include developing and managing distributed parallel data processing pipelines. This encompasses tasks such as configuring data connections, data parsing, data mapping and modeling, and data enrichment. You'll work closely with Integration Architects and Data Architects to ensure cohesive integration between systems and data models.

To qualify for the Data Engineer position at Accenture Federal Services, candidates should possess a Bachelor’s degree in a STEM field or have five years of relevant experience. Additionally, two years of experience in software engineering or as a data engineer, proficiency in programming languages like Python and Java, as well as experience with Agile methodologies and Git repositories are required.

Yes, an active TS/SCI US Government security clearance is mandatory for the Data Engineer role at Accenture Federal Services, due to the sensitive nature of the data you will be handling and the projects you'll be working on.

A Data Engineer at Accenture Federal Services should be familiar with distributed parallel streaming and batch data processing pipelines, as well as integrating with data SDKs and APIs. Knowledge of tools like GitHub, GitLab, and experience with Python / PySpark and Java / Java interfaces to Spark will also be beneficial.

Working as a Data Engineer at Accenture Federal Services means being part of an inclusive and collaborative environment. You'll find a culture that values diversity and encourages professional growth, providing you with the opportunity to develop and thrive in your career.

When answering this question, provide specific examples of data processing pipelines you’ve developed or maintained. Discuss the technologies and methodologies you employed, and how those experiences have prepared you for the role at Accenture Federal Services.

In your response, outline your familiarity with Agile principles and how you've implemented them in previous projects. Share specific scenarios where Agile practices improved project outcomes, demonstrating your adaptability and team collaboration.

Mention the programming languages you are proficient in, specifically Python and Java, and provide examples of projects where you've utilized these languages for tasks like data parsing, modeling, or building data pipelines.

Discuss specific challenges you've encountered during data integration processes, such as data consistency or quality issues, and describe the strategies you used to overcome them, illustrating your problem-solving skills.

Explain your approach to task prioritization, possibly by mentioning the use of Agile or Kanban methodologies, and how you ensure deadlines are met while maintaining the quality of your work.

Emphasize the significance of data normalization in reducing redundancy and ensuring data integrity. Provide examples of how you've applied normalization in past projects to enhance data quality.

Share your hands-on experience with CI/CD pipelines, detailing the tools you’ve used (like GitHub or GitLab) and how this continuous integration/deployment has improved your workflow in data projects.

If applicable, talk about your experience working in classified environments, focusing on the protocols and security measures you've adhered to, as well as the importance of maintaining data confidentiality.

Discuss the importance of documentation in data engineering, mentioning the types of documentation you create and your process for keeping them up-to-date, which is essential for team collaboration.

In your response, articulate your passion for data engineering, your alignment with Accenture Federal Services' mission, and how you envision contributing to their innovative projects that support the US federal government.