Senior Software Engineer, Data Authoring Platform
New
United StatesFull-TimeSenior
Salary196,000 - 230,000 USD per year
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonJavaSoftware ArchitectureAirflowGoData modelingdbtDistributed Systems
Requirements
- 5+ years of professional software engineering experience delivering production-grade systems.
- Strong programming skills in at least one modern language such as Python, Java, Go, or similar.
- Experience working at the intersection of software engineering and data engineering in complex distributed systems.
- Deep understanding of object-oriented design, API design principles, and scalable system architecture.
- Experience building or working with data platforms that include semantic layers, transformation frameworks, or data modeling systems.
- Proven ability to collaborate across multiple engineering teams and manage complex technical interfaces.
- Strong communication skills with a track record of leading or contributing to cross-functional technical initiatives.
- Experience working with data warehouses or large-scale analytical systems is a plus.
- Familiarity with tools or frameworks for data orchestration or abstraction (e.g., dbt, Airflow) is a bonus.
Responsibilities
- Design, build, and enhance core components of a large-scale data authoring and transformation platform used across the organization.
- Develop and extend services such as query rewriting, semantic modeling, and data pipeline abstraction layers.
- Improve the reliability, performance, and scalability of data infrastructure powering critical analytics and business metrics.
- Collaborate with cross-functional engineering teams to define and implement APIs and frameworks for data modeling and transformation.
- Participate in full software development lifecycle activities, including architecture design, implementation, testing, deployment, and on-call support.
- Research and evaluate emerging semantic layer techniques, data systems, and database engine capabilities to guide platform evolution.
- Enable internal teams to efficiently query, model, and experiment with data through intuitive and scalable tooling.
- Contribute to system-wide improvements in data governance, consistency, and usability across multiple abstraction layers.
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