Senior Data Engineer

Description

The company provides identity security for cloud enterprises, giving thousands of organisations clear visibility into their digital workforce and making sure people have the access they need, no more and no less.

The team is building a shared data platform: a lakehouse and streaming foundation that product teams use to ingest, store, process and serve identity data at SaaS scale. It feeds the company's identity graph and its analytics and ML products.

The platform supports the company's identity, risk and access products, including non-employee risk management, machine and agent identity security, data access security, and cloud infrastructure and entitlement management.

As a senior data engineer, you will design, build and run core parts of the platform: batch and streaming pipelines, warehouse and lakehouse models, and the data services product teams rely on. The shape of the data and the quality of the pipelines shape how fast, clear and trustworthy the products feel to customers.

This is a senior individual contributor role. You will own features and components end to end, raise the engineering bar for the team and mentor junior engineers.

In your first 30 days, you will onboard, learn the product and stack, meet stakeholders, set up your environment and take on scoped work on reliability, performance, modeling or pipeline hardening.

By 90 days, you will own features or pipelines end to end, contribute to design and reviews, and start mentoring others on dbt, Snowflake and pipeline practice.

By 6 months, you will be a trusted owner of one or more platform areas, show measurable gains in reliability, freshness or cost with before and after metrics, share on-call duty, help with escalations that have stalled others, and strengthen reviews, tooling, mentoring and docs.

Responsibilities

• Design, build and run scalable ETL and ELT pipelines and data services on the managed lakehouse.
• Take ownership of key pipelines and datasets from requirements through production support.
• Model the warehouse and lakehouse so they serve product features, analytics and ML.
• Shape data architecture by joining design discussions and writing and reviewing design documents.
• Improve performance, reliability and cost across Snowflake, Iceberg, Spark or Flink, and the storage and serving layers.
• Keep graph-serving models and access patterns responsive as data volume grows.
• Build data quality checks, governance, lineage and observability into the work you own.
• Work with product, infrastructure, security, analytics, data science and software engineering teams to ship practical solutions.
• Lead by example in code reviews, giving clear and useful feedback to peers.
• Mentor junior engineers and help shape team practices, tooling and onboarding.
• Use AI-assisted development to speed up delivery while keeping code review, security and data governance standards intact.
• Join the on-call rotation for data systems and turn incident learnings into lasting fixes.

Requirements

• Required: At least 5 years of professional experience building production software, with substantial data engineering work.
• Required: At least 2 years owning meaningful pipelines, datasets or data services in production.
• Required: Strong hands-on SQL, data modeling and query optimization skills, ideally on Snowflake or a comparable cloud warehouse.
• Required: Strong hands-on experience with dbt and Airflow, or close equivalents.
• Required: Proficiency in at least one of Python, Java or Scala.
• Required: Working experience with distributed batch and streaming processing, and production experience with at least one of Kafka, Flink, Spark or Cassandra.
• Required: Practical use of AI-assisted development tools such as GitHub Copilot, Cursor or Claude Code in a professional workflow covering data modeling, SQL and dbt, pipelines, testing and documentation, with disciplined review before production use.
• Required: Comfort working with cloud-native systems on AWS, Azure or GCP.
• Required: Knowledge of data governance, security and compliance best practices.
• Required: A record of delivering high-quality work independently and on time, including in unfamiliar parts of a codebase.
• Required: Strong communication skills, with the ability to write a clear design doc and explain tradeoffs to engineering and product partners.
• Required: BS in Computer Science, Engineering, or equivalent practical experience.
• Preferred: Background building data platforms at scale, including lakehouse patterns with Apache Iceberg.
• Preferred: Exposure to graph-oriented data systems and large relationship datasets; interest in billion-scale problems is a plus.
• Preferred: Experience with SLOs and modern observability for data systems such as Grafana, Prometheus or Datadog, and with instrumenting data jobs for performance, quality and cost.
• Preferred: Experience with Kubernetes, infrastructure as code such as Terraform or CloudFormation, and continuous delivery for data systems.
• Preferred: Operational tuning experience with Spark or Flink.
• Preferred: Experience with search and information retrieval systems such as OpenSearch.
• Preferred: Exposure to machine learning workflows and MLOps practices.
• Preferred: Familiarity with IAM/IGA, compliance, security or other privacy-sensitive product domains.

Conditions

Benefits and compensation vary based on work location and the nature of employment.

As part of total compensation, the role may be eligible for the company's corporate bonus plan or a role-specific commission, and potentially for equity participation.

Salary ranges are broad and reflect differences in knowledge, skills, experience, market conditions, location, products and lines of business. Placement within a range depends on these factors and on internal peer equity.

The estimated base salary for US-based employees is $108,600 - $183,018.00 USD.

Base salaries for employees in other locations are competitive for their home location.

Benefits

• Medical, dental and vision insurance.
• Short-term and long-term disability coverage.
• Life insurance and accidental death and dismemberment (AD&D) coverage.
• Supplemental life insurance for employees, spouses and children.
• Flexible spending accounts for health care and dependent care, plus a limited purpose flexible spending account.
• 401(k) savings and investment plan with company matching.
• Flexible vacation policy.
• 8 paid holidays a year.
• Sick leave.
• Paid parental leave.
• Employee assistance program and care counselors.
• Voluntary benefits: legal assistance, critical illness, accident, hospital indemnity and pet insurance.
• Health savings account with employer contribution.