SailPoint is building trusted analytics for HR and People Analytics, and the IT Data Engineering team needs an Analytics Engineer to design reliable data products end to end. This onsite role in the United States combines hands-on work in dbt and BigQuery with Tableau reporting, privacy controls, and practical troubleshooting. You will help ensure the metrics HR and leadership rely on are consistent, accurate, and delivered on time.
What you’ll do
- Design, build, and maintain modular dbt models in BigQuery to transform raw source data into analysis-ready datasets.
- Write performant, well-structured SQL, optimizing BigQuery queries and models for speed and cost.
- Build and support data ingestion pipelines from source systems and SaaS applications, including Workday, into the warehouse.
- Develop orchestration and scheduling for data workflows, and monitor pipeline runs to confirm data arrives on time and is complete.
- Define and standardize business metrics (including headcount, attrition, and recruiting funnel measures) so the same logic and numbers show up across reports and dashboards.
- Implement dbt tests, documentation, and code review practices to support data quality and maintainable models.
- Enforce data privacy and access controls for sensitive and PII data across models, sources, and dashboards, including Row-Level Security.
- Build and maintain Tableau data sources and dashboards using your models, including operational and executive-level views for HR and leadership.
- Support Tableau administration for owned content, including user permissions, publishing workflows, and refresh schedules.
- Build datasets for workforce analytics and forecasting use cases such as attrition and recruiting capacity planning.
- Troubleshoot data issues end to end, from source systems through transformation to reporting, driving root-cause fixes.
- Contribute to CI/CD, version control, and deployment practices for team data projects.
- Partner with business stakeholders to gather requirements, define KPIs, and scope new data products and visualizations.
- Help establish data modeling standards, naming conventions, and reusable patterns for the broader data team.
Technology and tools you’ll work with
dbt, BigQuery, SQL, Workday, Airflow, Cloud Composer, Tableau Desktop, Tableau Server, Tableau Cloud, Row-Level Security, Git, CI/CD, Tableau, Google Gemini, Google Cloud Platform
Required experience
- 5–8 years of experience in analytics engineering, data engineering, or a similar role.
- Advanced SQL, including window functions, CTEs, and query performance tuning.
- Hands-on experience with dbt for data models, including tests, macros, and documentation.
- Experience with a cloud data warehouse, preferably BigQuery.
- Experience with data pipelines and orchestration tools such as Airflow or Cloud Composer.
- Hands-on experience with Tableau Desktop and Tableau Server or Tableau Cloud, including published data sources, calculated fields, parameters, and Row-Level Security.
- Experience working with sensitive or PII data and applying privacy and access controls.
- Proficiency with Git and familiarity with CI/CD workflows.
- Strong understanding of data modeling concepts such as dimensional modeling, star schemas, granularity, and slowly changing dimensions.
- Ability to gather requirements from business users and translate them into data solutions.
- Strong attention to detail and a commitment to data accuracy.
- Clear written and verbal communication skills, including explaining technical concepts to non-technical and executive audiences.
Preferred background
- Bachelor’s degree in a STEM, business, or related field, or equivalent experience.
- Experience with HR or workforce data (headcount, attrition, recruiting funnel reporting).
- Experience with Workday data, including custom reports or integrations feeding downstream analytics.
- Experience using AI tools for analytical querying or building AI-assisted self-service reporting.
- Experience with Python for data pipelines, automation, or analysis.
- Experience with data ingestion tools such as Fivetran, Airbyte, or similar.
- Familiarity with Google Cloud Platform services beyond BigQuery.
- Experience in a SaaS or cybersecurity environment.
Compensation
USD 108,600 - 183,018 per year
Benefits
- Medical, dental, and vision insurance
- Short-term and long-term disability
- Life insurance and Accidental Death & Dismemberment (AD&D)
- Supplemental life insurance for employees, spouses, and children
- Flexible spending accounts for health care and dependent care; limited purpose flexible spending account
- 401(k) Savings and Investment Plan with company matching
- Flexible vacation policy
- 8 paid holidays annually
- Sick leave
- Paid parental leave
- Employee Assistance Program (EAP) and Care Counselors
- Voluntary benefits: Legal Assistance, Critical Illness, Accident, Hospital Indemnity and Pet Insurance options
- Health Savings Account (HSA) with employer contribution
Plan for success
- Days 1–30: Integration & Core Tech Onboarding
- Set up your developer environment and establish access to Google BigQuery, dbt, Git, and Tableau.
- Review active dbt models and data pipelines, including how data flows from systems such as Workday into BigQuery.
- Review security protocols for PII data, with emphasis on Row-Level Security in Tableau.
- Set up regular syncs with your manager, team peers, and key HR/People Analytics stakeholders.
- Days 31–60: Active Development & Core Deliverables
- Build, test, and deploy your first modular dbt model transforming raw workforce data in BigQuery.
- Collaborate with HR partners to define and codify a core business metric (for example, attrition or recruiting funnel metrics) within dbt.
- Develop, secure, and publish a targeted Tableau dashboard for an HR operational query.
- Participate in Git pull request reviews and support basic data pipeline monitoring.
- Days 61–90: Autonomous Ownership & Strategic Optimization
- Own an analytics project from requirements gathering to a final certified Tableau dashboard.
- Identify and optimize a complex BigQuery query or dbt model to improve processing speed and reduce warehouse costs.
- Coordinate an AI-assisted analytics prototype testing AI-assisted querying (such as Google Gemini on BigQuery) for business users.
- Extend analytical support to other business verticals as team priorities shift.
