Sr Data Engineer(SSIS + AWS Glue) || 4+ yrs || RR/1292/2024

As a Sr. Data Engineer at RR, your primary responsibilities will revolve around developing solutions with SSIS and AWS Glue, creating and optimizing stored procedures, and crafting efficient SQL queries. You will also be tasked with designing data models that align with the company's data strategies, fostering a strong data-driven culture within the organization.

To qualify for the Sr. Data Engineer role at RR, candidates should possess a minimum of four years of relevant experience in data engineering, demonstrating expertise in SSIS and AWS Glue. Proficiency in creating stored procedures, optimizing SQL queries, and a solid understanding of data modeling are also vital for this position.

Candidates applying for the Sr. Data Engineer position at RR should be well-versed in SQL for data manipulation and optimization. Additionally, familiarity with ETL processes, predominantly through SSIS and AWS Glue, is crucial. An understanding of other programming languages relevant to data engineering will be beneficial as well.

At RR, Sr. Data Engineers enjoy a dynamic and collaborative work environment. The hybrid model enhances flexibility, allowing professionals to experience both in-office synergy and remote work convenience. This culture promotes continuous learning and innovation among team members across various data projects.

Absolutely! Experience with cloud technologies, particularly AWS Glue as required for the Sr. Data Engineer position at RR, is essential. Cloud-based data solutions enable scalable, efficient management and integration of data, which is crucial for the company’s data initiatives.

In your response, focus on specific projects where you applied SSIS. Detail the types of packages developed, any challenges faced, and how your solutions improved data workflows. Highlight your ability to troubleshoot issues and optimize performance.

Discuss the techniques you use for query optimization, such as index creation, understanding execution plans, and refactoring complex queries. Provide examples from your past work where you achieved significant performance improvements.

Describe your methodology for data modeling, including requirements gathering, communicating with stakeholders, and utilizing modeling tools. Provide an example of a successful data model you've created and the impact it had on business decisions.

Elaborate on specific projects using AWS Glue, focusing on data wrangling, transforming datasets, and automated workflows. Describe how you've utilized Glue to integrate diverse data sources, and discuss any performance or scaling issues you resolved.

When crafting stored procedures, emphasize best practices, such as maintaining readability, performance tuning, and security. Cite examples of stored procedures you've developed that simplified data processing or enhanced application efficiency.

Think of a specific instance, articulate the challenge clearly, and provide a structured problem-solving approach you followed. Highlight your analytical skills, technical proficiency, and teamwork in arriving at a solution.

Discuss the resources you use, such as online courses, webinars, and data engineering communities. Mention your commitment to continuous professional development and how you implement new knowledge into your work.

Explain how you’ve implemented data governance best practices in your previous roles. Focus on ensuring data quality, managing access controls, and adhering to compliance standards relevant to data management.

Select a project that led to quantitative results—like reduced processing time or improved data accuracy. Provide specifics about your contribution and how it directly influenced the business objectives.

List additional tools or technologies you have experience with, such as other ETL tools, databases, or cloud services, and explain how you've utilized them in conjunction with SSIS and AWS Glue to achieve project goals.