Okay, I've searched for Data Engineer roles in Pittston, PA, full-time positions requiring 3+ years of experience in ETL pipeline development, strong Python and SQL skills, and experience with AWS cloud. I have found 74 positions for you.

Sure. Here's the analysis:

Job Analysis:

The Data Engineer III - Rx Systems at Chewy is fundamentally tasked with architecting and maintaining robust data pipelines that enable data-driven insights and operational improvements within Chewy’s pharmacy operations. This role requires a sophisticated understanding of complex data sets including customer, pet, order, and prescription data, highlighting the critical nature of accuracy and scalability in data workflows. The responsibilities cover end-to-end ownership—from strategy and design to deployment and monitoring—indicating a high degree of technical autonomy and accountability. Cross-functional collaboration is essential, as the engineer must translate stakeholder requirements into impactful data products while innovating to enhance data quality and process efficiency. Key qualifications emphasize proficiency in advanced SQL (notably in Snowflake), Python scripting for automation, and hands-on experience with cloud platforms, particularly AWS, reflecting the need to manage data solutions at enterprise scale. Familiarity with modern data engineering tools and methodologies (e.g., dbt, Apache Airflow) suggests the role demands continuous adaptation and innovation in technology use. Success here means delivering scalable, accurate, and timely data pipelines that empower data scientists and business teams, reinforcing operational decisions and improving customer experiences. The role also expects strong problem-solving in ambiguous situations, requiring independent initiative combined with collaborative skills to navigate complex system integrations and evolving business needs, making it a strategic function within the pharmacy operations team.

Company Analysis:

Chewy is a fast-growing, mission-driven leader in the pet e-commerce space, blending personalized service with technological innovation to support pet parents 24/7. The company’s culture is evidently customer-obsessed and innovation-focused, valuing employees who are passionate, empowered, and motivated to think big and deliver impact. This environment likely fosters agility and continuous learning—traits critical for a Data Engineer working in a dynamic operational setting like Chewy’s pharmacy division. The role sits at the intersection of technology and business, highlighting a collaborative culture that emphasizes cross-departmental interaction and data-driven decision-making. Chewy’s clear strategic emphasis on scaling quality and personalization in pet healthcare frames this data engineering role as pivotal to enabling insights that affect both operational efficiency and customer satisfaction. Working here implies embracing a sense of ownership and flexibility, along with the motivation to innovate within a company that prizes both technical excellence and a genuine commitment to its customer mission. Given Chewy’s size and trajectory, the candidate can expect to have significant exposure to the impact of their work and opportunities for growth within a supportive, mission-aligned team.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Tell me about a time when you identified a significant data quality issue in a pipeline. How did you approach resolving it, and what was the outcome?
• Behavioral: Describe an experience where you had to collaborate with multiple stakeholders who had differing data requirements. How did you manage those differing expectations?
• Behavioral: Share an example of a project where you automated a complex data workflow using scripting languages like Python. What challenges did you face and how did you overcome them?
• Behavioral: Can you talk about a situation where you had to learn and implement a new data engineering tool or technology quickly? How did you enable its adoption within your team?
• Technical: How do you approach designing an ETL/ELT pipeline that can scale with rapidly increasing data volumes and complexity?
• Technical: What strategies do you use to ensure data accuracy, consistency, and lineage in complex, multi-source data environments?
• Technical: Explain your experience working with cloud data warehouses such as Snowflake or Redshift. How do you optimize query performance and cost in these environments?
• Technical: What is your experience with tools like dbt or Apache Airflow, and how have you used them to manage data transformation workflows effectively?
• Situational: Imagine a critical data pipeline fails during peak operational hours, disrupting reporting for pharmacy operations. Walk me through your immediate actions and communication plan.
• Situational: If you received conflicting requirements from pharmacy operations and data science teams regarding a new data product, how would you handle prioritization and expectations?
• Situational: You notice recurring missing data in prescription records affecting analytics. How would you investigate the root cause and prevent future occurrences?
• Company Fit: What about Chewy’s mission and culture resonates most with you, and how would you see yourself contributing to our pharmacy operations team?
• Company Fit: How do you stay motivated and innovative in a fast-paced, evolving data environment like Chewy’s?
• Company Fit: Chewy emphasizes pet parents as family. How would you translate this customer-first philosophy in your approach to building data solutions?
• Questions for Interviewer: How does the pharmacy operations team currently measure the success of its data engineering initiatives?
• Questions for Interviewer: Can you describe the most significant data challenges Chewy’s pharmacy team is facing right now?
• Questions for Interviewer: What opportunities are there for professional growth within Chewy’s data engineering organization, particularly in the pharmacy domain?