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Senior Data Engineer
Ness Digital Engineering. Build scalable data pipelines using Python and Spark/PySpark .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building scalable data pipelines using Python and Spark/PySpark, with a strong focus on software engineering practices and production data solutions. Proficient in leveraging AWS, Databricks, and Snowflake for cloud-native data engineering.
Highest-signal resume keywords
Data Engineering ExperiencePython ProgrammingSpark/PySpark ExperienceAWS Solutions DeploymentDatabricks/Snowflake Proficiency
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data Pipeline DevelopmentPythonSparkPySparkData DebuggingData OptimizationProduction Data SolutionsCloud-Native Data EngineeringData OrchestrationSoftware Engineering Practices
Tools & Technologies
AWSDatabricksSnowflakeDagster
Industry Keywords
Financial Services
Tech Stack
Tools & technologiesAWSCloudPySparkPythonSpark
About the role
Key responsibilities & impact- Build scalable data pipelines using Python and Spark/PySpark
- Develop, debug, test, and optimize production data solutions
- Work with Databricks, Snowflake, and modern data platforms
- Build and deploy solutions on AWS
- Apply strong software engineering and coding practices
Requirements
What you’ll need- ~5 years of hands-on Data Engineering experience
- Strong Python-first programming and debugging skills
- Hands-on Spark/PySpark experience
- Experience with AWS and Databricks/Snowflake
- Production experience building and supporting data pipelines
- Financial Services domain experience (good to have)
- Dagster or other data orchestration platforms (good to have)
- Cloud-native data engineering experience (good to have)
Benefits
Comp & perks- Access to trainings and certifications
- Bonuses
- Aids
- Socializing activities
- Attractive compensation