Data Analytics Engineer - Customer Support
- Compensation
- €55.2K–€59.7K / yr
- Location
- Remote
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Data analytics
- Posted
Skills
Must have
- 3+ years of experience as a Data Analytics Engineer in Customer Support, contact-centre or CX environments.
- Strong SQL, dbt and hands-on experience with BI and reporting tools (we use QuickSight; Power BI, Looker or similar is fine).
- Solid knowledge of customer support metrics and contact-driver analysis: CSAT, FCR, handling time, repeat contact, deflection.
- Ability to turn insights into operational changes with non-technical stakeholders.
- Curiosity about why customers get in touch and what would stop the issue from happening again. You care more about changing outcomes than producing dashboards.
- Good written and spoken English (proficiency preferred).
Nice to have
- Python or text analytics on ticket data.
- Experience with a support CRM’s data
- Forecasting experience.
- Experience evaluating chatbots or AI agents.
- Background in digital goods or gaming.
Full description
About Eneba
At Eneba, we’re building an open, safe and sustainable marketplace for the gamers of today and tomorrow. Our marketplace supports close to 20m+ active users (and growing fast!), provides a level of trust, safety and market accessibility unparalleled to none. We’re proud of what we’ve accomplished in such a short time and look forward to sharing this journey with you. Join us as we continue to scale, diversify our portfolio, and grow with the evolving community of gamers.
About your team
Our Customer Support operation is scaling and becoming more automated. As our first dedicated Customer Support Data Analytics Engineer, you’ll sit in Operations and work hand in hand with the Customer Support team, the Head of Operations, Product, Engineering, Payments and Risk. You’ll explain why customers contact us, measure what self-service and automation actually resolve, and help the team decide what to fix first based on evidence.
You’ll work with Databricks (SQL), Kustomer, QuickSight, Mixpanel, Jira and Slack, on top of an internal data layer with Claude integrated. Current focus: AI and automation in support, and building and optimising our support metric stack.
Responsibilities
- Reusable data models: Build and maintain dbt models on Databricks across bronze, silver and gold layers, with tests, column documentation and incremental logic, so the next person can build on them.
- Data mapping and feasibility: Know what data we have, what is missing and how it connects. Join sources that were never designed to be joined, audit contact tagging so reporting stays reliable, and judge early whether a requested metric is buildable. If it isn’t, say exactly which data is missing and what it would take to get it.
- Metric design: Turn vague requests from the Customer Support team into exact metric definitions, such as repeat contact, automated deflection rate and cost per contact, and prove each one against raw data before it ships.
- Dashboards and reporting: Build dashboards end to end in QuickSight and Omni (datasets, calculated fields, filters). Deliver weekly support performance packs with actionable insights and thresholds for ops managers and leadership.
- Daily operations pulse: Monitor volumes, backlog, SLAs and CSAT to flag spikes early. Investigate “this number looks wrong” cases by tracing a visual back through the model to the source table.
- Contact-driver and feedback analysis: Identify why customers contact us by product, spot growing issues, and analyse QA and customer feedback to surface coaching themes and process gaps.
- Automation and self-service: Measure how well AI, bots and help content resolve issues, including repeat contacts and CSAT.