Senior Staff Software Engineer, Data
New
J
JobgetherData Platform
United StatesFull-TimeSenior
Salary$235,000–$285,000 USD
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Job Details
- Experience
- 15+ years
- Required Skills
- AWSPythonSQLJavaSparkScaladbt
Requirements
- Advanced degree in Computer Science, Engineering, or a related field.
- 15+ years of experience in data engineering, analytics engineering, or data platform roles.
- Proven experience architecting large-scale data and analytics systems in cloud environments.
- Strong hands-on expertise with modern data stacks and cloud data services across AWS, Azure, or GCP.
- Deep knowledge of analytics data modeling, including dimensional modeling, star and snowflake schemas, Data Vault, and related approaches.
- Advanced SQL skills and proficiency in Python, Scala, or Java.
- Advanced expertise in semantic layers and dimensional modeling, including technologies such as dbt or Cube, with the ability to provide agent-readable data context.
- Expertise with real-time streaming frameworks such as Spark, Flink, or Beam, combined with a strong understanding of batch and real-time architectures.
- Experience building reporting and business intelligence solutions at scale using tools such as Looker, Tableau, or Power BI.
- Strong understanding of data governance, security, privacy, lineage, metadata, and access-control best practices.
Responsibilities
- Define and own the end-to-end architecture strategy for data, analytics, and the Data Platform.
- Design scalable batch, streaming, and real-time data systems supporting structured and unstructured data.
- Establish standards for data modeling, semantic layers, reporting, governance, lineage, metadata, and data quality.
- Lead architecture reviews, technical decision-making, and adoption of modern approaches such as lakehouse, data mesh, and real-time analytics.
- Design and prototype critical platform components while writing production-quality code for complex and high-impact areas.
- Build AI-ready data infrastructure, including vector stores, embedding pipelines, retrieval systems, and RAG-ready architectures with strong lineage, governance, security, and observability.
- Develop a “Data for Agents” strategy that provides semantic layers and metadata enabling LLMs and AI agents to navigate enterprise data accurately.
- Partner with AI, product, and engineering teams on training datasets, feature stores, production inference pipelines, and agentic ETL/ELT workflows.
- Establish data governance, privacy, compliance, role-based access controls, auditability, validation processes, and quality frameworks.
- Mentor senior engineers, analytics engineers, and data scientists while partnering across product, ML, platform, and business teams.
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