Data Engineer II
Content + Source + Freshness • 14 Dec 2025 • 95% confidence
Offer value
High value due to the significant demand for data engineering, excellent compensation potential, and leadership opportunities in a progressive environment.
- Lead a team of engineers in a cutting-edge data environment.
- High demand for data engineering skills in the market.
- Engagement with innovative data tools and frameworks.
- Requires significant technical expertise and leadership experience.
Pros
- Opportunity to lead and mentor a team of data engineers.
- Engagement with state-of-the-art data technologies.
- High compensation prospects in the data engineering field.
Cons
- High responsibility involving leading complex projects.
- Fast-paced environment can be overwhelming.
- Expectation for continuous learning and adaptation to new tools.
Who it's for
Senior / Lead • On-site or hybrid possible
Good fit
- Senior data engineers with leadership aspirations.
- Professionals interested in advanced data technologies.
- Individuals motivated by team collaboration and innovation.
Not recommended for
- Entry-level data professionals.
- Those who avoid leadership responsibilities.
- Candidates looking for positions with minimal technical challenges.
Motivation fit
Key skills
About the job
Our Opportunity:
We are looking for a highly experienced and forward-thinking Data Engineer to join our Enterprise Data Systems team, which drives reporting, analytics, and data science capabilities for numerous teams across the organization. In this key role, you will be responsible for designing and building a state-of-the-art data platform that will serve as the foundation for data processing and analysis for the foreseeable future. The ideal candidate will bring extensive experience in developing large-scale data ingestion and processing frameworks, coupled with a strong command of data engineering best practices, while also providing strategic leadership and mentorship to the team.
Key Responsibilities:
- Lead the design, development, and optimization of scalable, efficient, and reusable data ingestion and processing frameworks using Spark and Spark Streaming.
- Architect, implement, and manage data pipelines using a variety of tools, including Kafka, Kinesis, Airflow, and AWS services such as Glue, EMR, S3, SQS, SNS, and Step Functions.
- Guide and mentor the data engineering team, setting best practices, technical guidelines, and guardrails to ensure high-quality data solutions.
- Collaborate with cross-functional teams, including data science and analytics, to design and build data platforms that support the organization’s evolving data needs.
- Ensure data quality, observability, and governance by implementing best-in-class practices for data auditing, validation, and monitoring.
- Hands-on experience working with data warehouse platforms such as Snowflake, Redshift, and relational databases like DynamoDB and PostgreSQL.
- Develop and manage orchestration pipelines using Airflow to optimize job scheduling and improve operational efficiency.
- Drive innovation by evaluating new data tools, technologies, and frameworks to enhance the company’s data infrastructure.
- Implement and maintain CI/CD pipelines and infrastructure-as-code using Terraform for fast, efficient deployments.
- Collaborate on cloud architecture decisions and drive the use of AWS services to manage large-scale data processing.
- Partner with stakeholders across the organization to understand their data needs and deliver reliable, high-quality data products.
Qualifications:
- Proven experience as a Lead Data Engineer or Senior Data Engineer in a data-driven environment.
- Highly skilled and hands-on team member to design, and develop data pipelines using Python, Spark, Spark streaming and Kafka for building large-scale data processing frameworks.
- Proficiency in cloud data platforms like AWS Glue, EMR, S3, SQS, SNS, and Step Functions.
- Strong working knowledge of Snowflake, Redshift, DynamoDB, PostgreSQL, and other data warehouses/databases.
- Experience with Airflow for orchestration and automation of data workflows.
- Deep understanding of data quality, governance, and observability principles.
- Knowledge of CI/CD pipelines and terraform for cloud infrastructure management.
- Strong knowledge of data platforms such as AWS Data platform, Databricks, Cloudera.
- Ability to work in a fast-paced, dynamic environment, with excellent problem-solving and leadership skills.
Nice to Have:
- Familiarity with Databricks or Cloudera platforms.
- Experience setting up and deploying data infrastructure using Terraform.
- Previous experience working with CI/CD pipelines to streamline deployment processes.
Why Join Us: You will be a key player in building a data platform that supports a data-driven organization, impacting reporting, analytics, and machine learning. You’ll work with cutting-edge tools and lead a high-performing team, helping shape the future of data infrastructure within the company.
Chewy is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, gender, citizenship, marital status, religion, age, disability, gender identity, results of genetic testing, veteran status, as well as any other legally-protected characteristic. If you have a disability under the Americans with Disabilities Act or similar law, and you need an accommodation during the application process or to perform these job requirements, or if you need a religious accommodation, please contact CAAR@chewy.com.
To access Chewy's Customer Privacy Policy, please click here.
To access Chewy's California CPRA Job Applicant Privacy Policy, please click here.
