We're hiring a Senior Data Engineer \/ Data Analyst to own our data end to end: the pipelines into ClickHouse and BigQuery, our migration to ClickHouse, and the analytics supporting our social casino and mystery-box ecommerce businesses.
About the Company
We develop social casino games and mystery-box ecommerce applications for web and mobile. Our social casino products operate on a virtual-coin economy with live ops, missions, and promotions. We are also developing mystery-box ecommerce brands where customers purchase boxes containing real physical products.
Data drives our product decisions: which players and buyers to acquire, which promotions to run, and how to keep each economy healthy. You'll be responsible for making that data reliable and turning it into actionable insights.<\/p>\n
What You'll Do
Build and Run the Pipelines
Own ingestion into ClickHouse and BigQuery from PostgreSQL and MongoDB change-data-capture, client and server events, payment providers, and AppsFlyer.
Run and extend the Airflow DAGs that build our bronze, silver, and gold layers. Make every job idempotent, backfillable, and monitored.
Add freshness and quality checks, and alert when a table is late or wrong.
Make ClickHouse our main analytics warehouse in place of BigQuery, starting with our mystery-box products.
Design ClickHouse schemas, sort keys, materialized views, and retention for events, orders, and wallet data.
Migrate pipelines, models, and dashboards, and run both warehouses side by side until the numbers match.
Keep BigQuery reliable and cost-controlled while the social casino products still run on it.
Build the Business Analytics
Maintain the KPI layer leadership uses to run the company: DAU, revenue, retention, LTV, ROAS, and economy health.
Move reporting off Tableau and older analytics tools onto our own AI dashboards, built in-house on the tables you create.
Answer ad hoc questions for product, live ops, marketing, and finance.
Design A\/B tests and promotion experiments, and interpret their results accurately.
Work with the Product Teams
Agree on event schemas and tracking with backend and client engineers before a feature ships.
Define metrics consistently and document every table so all teams and AI dashboards report the same numbers.<\/p>\n
Questions You'll Answer
These are examples of the questions you'll own, from the pipeline to the answer.
Social Casino
Which acquisition sources pay back, and by which day?
How do missions, promotions, and coinback change retention and spend?
Is the virtual-coin economy inflating?
Where do new players drop off in their first session?
Mystery-Box Ecommerce
Which boxes convert from view to purchase, and why?
How does the value of the products received compare with box prices across different tiers?
Do referrals and promotions bring repeat buyers or one-time discount hunters?
Which countries and devices grow fastest?
Company
What is one revenue and margin view across all products for the CEO and CFO?
Which metrics should every team report the same way?
Our Data Stack
Sources: PostgreSQL (transactions, wallets, payments), MongoDB (missions, segmentation, analytics projections), client and server events, AppsFlyer.
Ingestion: Change-data-capture mirrors of PostgreSQL and MongoDB into ClickHouse and BigQuery; Airflow for scheduled jobs.
Main Warehouse: ClickHouse, replacing BigQuery, with mystery-box products moving first. It also holds our telemetry through OpenTelemetry (ClickStack).
Legacy Warehouse: BigQuery, organized in bronze, silver, gold, and semantic layers until the migration is complete.
Cloud: Google Cloud and AWS.
Products: TypeScript backends, React web apps, native mobile apps.
Dashboards: In-house AI dashboards built on ClickHouse and BigQuery tables, replacing Tableau and older analytics tools.<\/p>\n
What You Bring
6+ years of experience in data engineering, analytics engineering, or a data analyst role where you built and owned production pipelines.
Expert SQL, including window functions, cohort queries, and query-plan tuning.
Production ClickHouse: MergeTree table engines, sort keys, materialized views, and TTLs. Having led a warehouse migration is a strong plus.
Production experience with BigQuery: partitioning, clustering, scheduled jobs, and cost control.
Python for pipelines, and an orchestrator such as Airflow. dbt or a similar modeling tool is a plus.
Change-data-capture or event streaming from operational databases (for example, Datastream, Debezium, Pub\/Sub, or Kafka).
Data modeling you can defend: layered or dimensional models, slowly changing dimensions, safe backfills.
Product analytics: retention cohorts, LTV, ARPDAU, funnels, and A\/B test design with a correct read of significance.
Analytics that go beyond a BI tool: you design the tables, metric definitions, and queries an AI dashboard can answer from correctly. Tableau experience helps with the migration.
English: Upper-Intermediate (B2) or higher.<\/p>\n
Nice to Have
Social casino, free-to-play, or mobile gaming analytics: virtual-currency economies, live ops, payer conversion.
Ecommerce or mystery-box analytics: conversion funnels, average order value, repeat purchase, box value versus price, promotion and referral attribution.
Mobile measurement partner data (AppsFlyer or similar), user acquisition, and ROAS modeling.
OpenTelemetry or ClickStack, and observability data at volume.
Infrastructure as code (Terraform) on Google Cloud or AWS.
Enough TypeScript to add or fix an event at the source.
Privacy-safe data handling: PII separation and account-deletion requests.<\/p>\n","protected":false},"excerpt":{"rendered":"
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