Senior Staff Software Engineer, Data Delivery

New

JobgetherData and Analytics

Based in United StatesFull-TimeSenior

SalaryCompetitive U.S. base salary range of $240,000–$330,000

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Job Details

Extensive experience shaping technical direction across multiple teams, domains, or large-scale platform areas, with the seniority and judgment expected of a Staff or Senior Staff-level engineer.
Deep expertise building scalable backend services, distributed systems, data platforms, or data-intensive applications in production environments.
Strong system-design capabilities across APIs, data pipelines, databases, distributed systems, observability, reliability, and long-term architecture.
Highly effective at influencing engineering, product, data science, analytics, and business stakeholders and aligning them around complex technical strategies.
Ability to design reusable systems and capabilities that create leverage across multiple teams.
Strong data-quality mindset and ability to reason about correctness, freshness, completeness, consistency, explainability, cost efficiency, and customer trust.
Strong product and business judgment, connecting engineering investments to customer outcomes and long-term value.
Proven experience mentoring senior engineers, raising engineering standards, and influencing technical culture.
Strong programming skills, with experience in Golang preferred.
Familiarity with technologies such as Kafka, TiDB, Redshift, Vitess, Iceberg, StarRocks, or Trino is a plus.

Shape the long-term architecture, technical strategy, and engineering direction for Data Delivery and related data and analytics systems.
Lead high-impact initiatives spanning multiple teams, systems, and product areas, helping engineering groups align around scalable and sustainable technical solutions.
Build and guide platform capabilities that enable teams to deliver new reporting, measurement, forecasting, billing, export, and analytics functionality more efficiently.
Make critical system-design decisions across APIs, data pipelines, data models, distributed systems, storage technologies, serving layers, and operational patterns.
Partner with product, engineering, data science, analytics, and business leaders to translate organizational priorities into actionable technical roadmaps.
Identify systemic opportunities across reliability, scalability, data quality, observability, developer experience, and operational ownership, then drive improvements across the relevant technical areas.
Mentor and coach engineers, including senior-level contributors, through architecture reviews, design discussions, technical guidance, and hands-on leadership.

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