Data Warehouse Engineer

As a Data Warehouse Engineer at ECP, you will be responsible for designing, developing, and maintaining enterprise data warehouses, along with creating and managing data pipelines using tools like AWS Glue and AWS DMS. You will implement ETL processes, optimize performance, and troubleshoot data quality issues while working closely with data analysts to meet their data needs. Your expertise in SQL and data modeling techniques will be critical to enhancing our product offerings and improving overall data management strategies.

Candidates for the Data Warehouse Engineer position at ECP should possess a Bachelor's Degree in Computer Science or a related field, alongside at least 4 years of software development experience. Strong proficiency in SQL and database design is crucial, as well as familiarity with AWS services such as Redshift, DMS, and Glue. Knowledge of ETL processes, data modeling, and scripting languages like Python or Shell is also required to effectively contribute to our team's success.

In the role of Data Warehouse Engineer at ECP, you will work with a variety of state-of-the-art tools and technologies, including AWS Redshift for data warehousing, AWS Glue for ETL processes, and AWS Database Migration Service (DMS) for data pipeline management. Familiarity with complex SQL, data quality tools, and various scripting languages will also be beneficial as you maintain and optimize our data warehouse environment.

Absolutely! ECP believes in continuous learning and development, especially for our Data Warehouse Engineers. You will have constant opportunities to refine your skills, dive into new technologies, and work on innovative projects that impact our customer base. Our collaborative environment encourages knowledge sharing, ensuring you enhance your expertise while contributing to meaningful solutions in the senior living sector.

ECP fosters a friendly and inclusive company culture where collaboration and innovation thrive. As a Data Warehouse Engineer, you'll be part of a committed team that values each contribution and focuses on mutual growth. We encourage open communication, provide flexibility for remote work, and emphasize a healthy work-life balance, enabling you to enjoy unlimited PTO while making a real impact in our industry.

When answering this question, highlight specific projects where you've implemented or optimized AWS Redshift, emphasizing your role in building data warehouses. Discuss any challenges you faced and how you overcame them, such as optimizing query performance or managing access controls. Your aim should be to showcase your hands-on experience and understanding of effective data management within AWS.

In your response, explain the steps you take to implement data quality checks and monitoring processes. Highlight any tools or practices you use, such as automated testing or validation routines. It’s important to illustrate your proactive approach to identifying and resolving data issues, ensuring that the data being analyzed is accurate and reliable.

Discuss specific optimization techniques you've employed, such as rewriting complex SQL queries, indexing strategies, and partitioning data. Mention how you've measured performance improvements and the impact of these enhancements on overall data accessibility. It’s critical to demonstrate your analytical skills and problem-solving approach to maintaining performance.

Share a particular project where you've utilized dimensional or snowflake schema designs. Explain the context and your rationale for choosing that model, focusing on how it aligned with the business requirements and improved data retrieval times. This will illustrate your practical knowledge and decision-making skills in data modeling.

Discuss your methods for continuing education, whether it's attending industry conferences, participating in webinars, following relevant blogs, or being part of professional networks. Mention any certifications or courses that you've pursued to deepen your expertise in data warehousing. This reflects your commitment to professional growth and staying at the forefront of technology.

Detail your hands-on experience with various ETL tools and methods you've used for data integration and transformation in previous roles. Highlight specific examples of ETL workflows you've built or optimized, and your understanding of how these processes fit into the larger data warehouse architecture. Be sure to convey the impact of your work on overall data quality and accessibility.

Outline your methodical approach to diagnosing and resolving data quality issues. Explain how you would gather relevant stakeholders, assess the problem, and implement corrective actions while ensuring minimal disruption to operations. Demonstrate your problem-solving mindset and communication skills by emphasizing the importance of cross-team collaboration in such scenarios.

Describe your commitment to maintaining clear and comprehensive documentation throughout your projects. Emphasize how documentation aids in knowledge transfer, supports team collaboration, and ensures compliance with data governance standards. Share examples of how good documentation has benefitted past projects you’ve worked on.

Discuss your experience with task prioritization and project management methodologies, such as Agile or Kanban. Explain how you assess project timelines, align with team goals, and communicate any changes or recommendations to stakeholders. Providing concrete examples of past scenarios where effective prioritization made a difference will strengthen your response.

Reflect on your experience and identify key skills, such as SQL proficiency, knowledge of data modeling, and experience with cloud services like AWS. Discuss soft skills too—like analytical thinking, attention to detail, and collaborative abilities—as they are crucial for working effectively in a team environment. Your answer should illustrate a comprehensive understanding of what contributes to success in this role.