Senior Analytics Engineer

The Data Science & Analytics team at Asana is how the company turns data into decisions — defining the questions that matter, surfacing the answers, and making sure insight is at the center of every critical product and business call. As a Senior Analytical Engineer, you sit at the intersection of Data Engineering, Analytics, and Data Science, and you own the data foundations for a business domain end to end. Your mandate is to turn raw data into reliable, business-ready datasets that PMs, analysts, data scientists, and leaders actually trust and use — and to define the business logic and metric standards that make AI-powered self-serve trustworthy. You consume governed Silver tables and produce the Gold layer and semantic layer beneath Asana's most important metrics, dashboards, and Genie spaces.

This role is based in our Warsaw office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday, with the option to work from home on Wednesdays and, depending on the work and the teams you partner with, on Fridays. If you're interviewing for this role, your recruiter will share more about the in-office expectations.

We know that the best ideas and solutions come from multi-dimensional teams. That's because these teams reflect a variety of backgrounds and professional experiences. If you are excited about this role and feel your experience can make an impact, please don't be shy - apply today.

At Asana, we're committed to building teams that include a variety of backgrounds, perspectives, and skills, as this is critical to helping us achieve our mission. If you're interested in this role and don't meet every listed requirement, we still encourage you to apply.

What we’ll offer

• Generous, transparent and fair compensation system (base salary and RSUs)
• Contract of Employment (and the option of 50% tax deductible costs for author’s rights usage in respect of applicable roles )
• Health insurance with dental and travel coverage (Lux Med)
• Breakfast and lunch catering on the days that you work from the office
• Vacation allowance
• Career growth budget
• Home office setup budget
• Gym/Fitness card
• Fertility healthcare and family-forming support with Carrot
• Mental Health Support in Modern Health
• Group life insurance
• MacBooks with all necessary accessories

For this role, the estimated base salary range is between 20,833 - 23,700 PLN gross per month (subject to all taxes and necessary deductions). The actual base salary will vary based on various factors, including market and individual qualifications objectively assessed during the interview process. The listed range above is a guideline, and the base salary range for this role may be modified.

In addition to base salary, your compensation package may include additional components such as equity and sales incentive pay (for most sales roles), and benefits. If you're interviewing for this role, speak with your recruiter to learn more about the total compensation and benefits for this role.

#LI-Hybrid

• 4+ years in analytics engineering, data engineering, or a closely related analytics role, with a track record of independently owning the data models a team relies on for decisions.
• Advanced SQL and strong data modeling fundamentals: dimensional modeling, star/snowflake schemas, slowly changing dimensions, and semantic layer design.
• Hands-on experience with a transformation framework (dbt or equivalent), orchestration tooling (e.g. Airflow), version control (Git), and modern warehouse/lakehouse platforms (Databricks experience preferred).
• Practical experience with data quality testing and observability, schema management and data contracts, and query/model performance and cost tuning.
• Demonstrated domain fluency in at least one business area (e.g. PLG funnels, SLG pipeline, marketing attribution, Product telemetry, revenue/ARR) and the judgment to translate "I don't trust this number" into a specific, durable model fix.
• Strong cross-functional partnership skills: requirement gathering, prioritization, documentation and enablement, driving alignment on metric definitions, and explaining technical tradeoffs to non-technical partners.
• Curiosity about AI-native analytics — NL2SQL, metadata/semantic layers for self-serve, and using tools like Claude and Genie to multiply your reach rather than replace rigor. Exposure to Unity Catalog, Looker/LookML, or reverse-ETL/activation (Salesforce, Marketo, Gainsight) is a plus.