Senior Staff Software Engineer, Data

New

JobgetherData platforms

US; based in United StatesFull-TimeStaff

SalaryBase salary, equity, and a comprehensive benefits package. U.S. base salary range of $235,000–$285,000 USD

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Job Details

Experience: 15+ years of experience in data engineering, analytics engineering, data platforms, or closely related roles.
Required Skills: AWSPythonSQLBusiness IntelligenceGCPJavaAzureSparkScalaData modelingdbt

Hold an advanced degree in Computer Science, Engineering, or a related field.
Bring 15+ years of experience in data engineering, analytics engineering, data platforms, or closely related roles.
Have experience architecting and scaling large data and analytics systems in cloud environments.
Demonstrate deep expertise in analytics data modeling, including dimensional modeling, star/snowflake schemas, Data Vault, and semantic layers.
Have advanced SQL skills and strong proficiency in Python, Scala, or Java.
Have hands-on experience with modern data stacks and cloud data services across AWS, Azure, or GCP.
Understand batch, streaming, and real-time architectures, with expertise in frameworks such as Spark, Flink, or Beam.
Have experience designing semantic layers or metrics stores using technologies such as dbt or Cube.
Have experience building reporting and business intelligence solutions at scale, with familiarity with Looker, Tableau, or Power BI.
Understand data governance, lineage, metadata, privacy, security, access controls, and auditability.
Be able to operate at executive and deeply technical levels and demonstrate communication, collaboration, and technical leadership skills.

Define and own the end-to-end architecture strategy for data and analytics, including batch, streaming, and real-time systems.
Lead the transformation toward a modern Data Platform using lakehouse, data mesh, real-time analytics, self-service access, and reusable data products.
Design and prototype platform components and write production-quality code for complex, high-impact areas.
Establish standards for data modeling, semantic layers, reporting, metadata, lineage, governance, and data quality.
Build scalable structured and unstructured data infrastructure, including ingestion, transformation, APIs, vector stores, embeddings, retrieval systems, and RAG-ready architectures.
Develop governed, discoverable enterprise data capabilities for LLMs and AI agents.
Partner with AI, product, and engineering teams on training datasets, feature stores, inference pipelines, and agentic ETL/ELT workflows.
Ensure platform reliability, availability, observability, security, disaster recovery readiness, performance, and cost efficiency.
Develop scalable models and enable trustworthy, performant, accessible analytics and business intelligence.
Mentor senior engineers, analytics engineers, and data scientists, and establish operational standards including SLAs/SLOs, monitoring, alerting, incident response, and capacity planning.

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