Data Engineer (Pyspark/Snowflake/ AWS)
Content + Source + Freshness • 14 Dec 2025 • 95% confidence
Offer value
High value role emphasizing cloud technologies and data engineering, with robust compensation.
- Focus on contemporary data engineering practices and cloud tech
- Strong salary range with substantial growth potential
- Roles in high-demand areas such as AWS and Pyspark
Pros
- Salary potential in high demand for Data Engineers specializing in AWS
- Focus on innovative data solutions leveraging modern tech
- Opportunity to contribute to impactful data transformation projects
Cons
- Requires extensive technical knowledge limiting some applicants
- Fast-paced and demanding work environment
- Work hours may require flexibility based on project deadlines
Who it's for
Mid to Senior-level • Full-time, hybrid possible
Good fit
- Skilled data engineers with cloud expertise
- Candidates interested in high-impact data projects
- Professional who want to leverage tech for data transformation
Not recommended for
- New graduates without applicable skills
- Those unfamiliar with data processing frameworks
- Professionals preferring low-technical engagement
Motivation fit
Key skills
About the job
Role Overview:
We are seeking an experienced Data Engineer with expertise in PySpark, Snowflake and AWS to join our team. The ideal candidate will design, develop, and optimize data pipelines and workflows in a cloud-based environment, leveraging AWS services, PySpark, and Snowflake for data processing and analytics.
Key Responsibilities:
Design and implement scalable ETL pipelines using PySpark on AWS.
Develop and optimize data workflows for Snowflake integration.
Manage and configure AWS services such as S3, Lambda, Glue, EMR, and Redshift.
Collaborate with data analysts and business teams to understand requirements and deliver solutions.
Ensure data security and compliance with best practices in AWS and Snowflake environments.
Monitor and troubleshoot data pipelines and workflows for performance and reliability.
Write efficient, reusable, and maintainable code for data processing and transformation.
Required Skills:
Strong experience with AWS services (S3, Lambda, Glue, MSK, etc.).
Proficiency in PySpark for large-scale data processing.
Hands-on experience with Snowflake for data warehousing and analytics.
Solid understanding of SQL and database optimization techniques.
Knowledge of data lake and data warehouse architectures.
Familiarity with CI/CD pipelines and version control systems (e.g., Git).
Strong problem-solving and debugging skills.
Experience with Terraform or CloudFormation for infrastructure as code.
Knowledge of Python for scripting and automation.
Familiarity with Apache Airflow for workflow orchestration.
Understanding of data governance and security best practices.
Certification in AWS or Snowflake is a plus.
Education and Experience:
Bachelor’s degree in Computer Science, Engineering, or related field with 6 to 10 years of experience.
5+ years of experience in AWS cloud engineering.
2+ years of experience with PySpark and Snowflake.
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Job Family Group:
Technology------------------------------------------------------
Job Family:
Digital Software Engineering------------------------------------------------------
Time Type:
Full time------------------------------------------------------
Most Relevant Skills
Please see the requirements listed above.------------------------------------------------------
Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.------------------------------------------------------
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