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Senior Data Engineer
| Company | Glassdoor |
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| Location | Atlanta, GA, USA |
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| Salary | $112000 – $148800 |
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| Type | Full-Time |
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| Degrees | Bachelor’s |
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| Experience Level | Senior |
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Requirements
- 5+ years of experience developing scalable, resilient data engineering solutions
- 3+ years of hands-on experience with distributed data processing and cloud technologies (e.g., Spark, Flink, Kafka, Snowflake, Databricks, Redshift)
- 4+ years of experience coding in Python and applying software engineering best practices
- Deep understanding of distributed data processing, data modeling, and building ETL/ELT pipelines
- Familiarity with data architecture patterns (Lambda vs. Kappa), OLTP vs. OLAP systems, and data modeling strategies
- Exposure to test-driven development and automated testing frameworks
- Experience working in Agile/Scrum environments
- Strong communication and collaboration skills—able to work across functions and explain technical concepts to non-technical audiences
- Bachelor’s degree in Computer Science or equivalent professional experience
Responsibilities
- Design, build, and maintain scalable batch and streaming data pipelines using technologies like Apache Airflow, Spark, Flink, Kafka, Iceberg, and Snowflake
- Develop real-time data workflows using engines such as Kafka or Kinesis
- Collaborate with cross-functional teams—product managers, software engineers, ML engineers, and data scientists—to design data models and pipelines that support business use cases
- Leverage AI tools to improve development velocity, data quality, and platform reliability
- Drive the evolution of our data platform, ensuring efficiency, resiliency, and scalability
- Apply software engineering best practices, including unit and integration testing, to data workflows
- Participate in a rotational on-call schedule to support production systems
- Maintain and enhance existing systems to meet evolving business needs
- Quickly ramp up on our technology and domain, and proactively share knowledge with the team
Preferred Qualifications
- Experience building customer-facing products, machine learning pipelines, or data products
- Familiarity with CI/CD pipelines, containerization (Docker, Kubernetes), and cloud platforms (AWS, GCP)
- Exposure to modern data tools like DBT, Soda Spark, Great Expectations, Anomalo, or Monte Carlo
- Experience with observability and alerting tools such as DataDog
- Contributions to open-source projects or active involvement in the data engineering community
