Data and Operations Engineer

RemoteStar; San Francisco (CA); On-site; USD 210,000 - 320,000; Full time

Job summary

RemoteStar in San Francisco seeks a Staff/Principal Data and Operations Engineer to lead the design, scalability, and reliability of core data infrastructure and operational systems. This senior role combines hands-on building with technical leadership and cross-team collaboration to scale data platforms for the business.

You will own end-to-end data pipelines, monitoring, incident response, and automation, partnering with Product, Data Science, and Infrastructure to shape engineering standards

Qualifications

• 8+ years of experience in data engineering, platform engineering, or infra/operations roles with Staff/Principal-level scope.
• 4+ years in fintech.
• Deep expertise in distributed data systems (Spark, Kafka, Airflow, dbt) and cloud data warehousing (Snowflake, BigQuery, Redshift).
• Strong proficiency in Python, SQL, and at least one systems language (Go, Java, or Scala).
• Experience with cloud infrastructure (AWS, GCP, or Azure) and infrastructure-as-code (Terraform).
• Track record of leading large-scale technical initiatives with cross-functional stakeholders.
• Strong operational mindset with observability tooling (Datadog, Grafana, Prometheus) and incident management.
• Excellent communication skills and mentoring experience for senior engineers.

Responsibilities

• Architect, build, and scale data pipelines and infrastructure supporting analytics, ML, and product use cases.
• Own operational reliability — uptime, observability, incident response, and on-call practices for data and platform systems.
• Set technical direction and best practices for data engineering and operations across the company.
• Partner with Product, Data Science, and Infrastructure teams to translate business needs into scalable technical solutions.
• Drive automation initiatives to reduce manual operational overhead.
• Mentor and provide technical leadership to engineers at all levels; act as a force multiplier across teams.
• Lead architecture reviews, design docs, and technical decision-making for high-impact systems.
• Identify and resolve systemic bottlenecks in data quality, pipeline performance, and operational workflows.

Job description

Data and Operations Engineer — Staff/Principal Lead

About the Role

Looking for a Staff/Principal-level Data and Operations Engineer to lead the design, scalability, and reliability of our core data infrastructure and operational systems. This is a senior technical leadership role for someone who can operate as both a hands‑on builder and a force multiplier — setting technical direction, mentoring engineers, and partnering closely with cross‑functional teams to ensure our data platforms and operational tooling scale with the business.

You’ll own critical systems end‑to‑end: from data pipeline architecture to operational excellence practices (monitoring, incident response, automation), and you’ll be a key voice in shaping engineering standards across the org.

What You’ll Do

• Architect, build, and scale data pipelines and infrastructure supporting analytics, ML, and product use cases
• Own operational reliability — uptime, observability, incident response, and on‑call practices for data and platform systems
• Set technical direction and best practices for data engineering and operations across the company
• Partner with Product, Data Science, and Infrastructure teams to translate business needs into scalable technical solutions
• Drive automation initiatives to reduce manual operational overhead
• Mentor and provide technical leadership to engineers at all levels; act as a force multiplier across teams
• Lead architecture reviews, design docs, and technical decision‑making for high‑impact systems
• Identify and resolve systemic bottlenecks in data quality, pipeline performance, and operational workflows

What We’re Looking For

• 8+ years of experience in data engineering, platform engineering, or infrastructure/operations roles, with demonstrated Staff/Principal-level scope
• 4+ years in fintech
• Deep expertise in distributed data systems (e.g., Spark, Kafka, Airflow, dbt) and cloud data warehousing (Snowflake, BigQuery, Redshift)
• Strong proficiency in Python, SQL, and at least one systems language (Go, Java, or Scala)
• Proven experience with cloud infrastructure (AWS, GCP, or Azure) and infrastructure‑as‑code (Terraform)
• Track record of leading large‑scale technical initiatives with cross‑functionally stakeholders
• Strong operational mindset — experience with observability tooling (Datadog, Grafana, Prometheus), incident management, and building resilient systems
• Excellent communication skills and experience mentoring senior engineers
• Comfortable operating with high autonomy and ambiguity in a fast‑paced environment