社招， 小而美的外企， work life balance， 每年有海外团建， 该岗位base地在北京(国贸), 有意者请发英文简历到公司邮箱： ycui@liftoff.io ， 谢谢
下面是JD：
Staff Analytics Engineer, Data
Beijing
Liftoff is a leading AI-powered performance marketing platform for the mobile app economy. Our end-to-end technology stack helps app marketers acquire and retain high-value users, while enabling publishers to maximize revenue across programmatic and direct demand.
Liftoff’s solutions, including Accelerate, Direct, Monetize, Intelligence, and Vungle Exchange, support over 6,600 mobile businesses across 74 countries in sectors such as gaming, social, finance, ecommerce, and entertainment. Founded in 2012 and headquartered in Redwood City, CA, Liftoff has a diverse, global presence.
At Liftoff, we’re solving a core challenge for mobile apps: growth. We build machine learning and big data technology that predicts which apps users will enjoy, connecting them in a meaningful way. Our systems process over 4 million requests per second and interact with more than a billion users weekly, operating at a scale seen only in the largest tech companies. We’re profitable, innovative, and experiencing rapid growth.
We’re looking for a staff-level analytics engineer to lead high-impact data products and our BI/modeling stack, partnering across Supply to ship reliable, cost-efficient, and scalable solutions. You’ll be self-directed, help set technical direction, and drive multi-quarter epics to completion while mentoring others.
Location
This role is office-based with 3 days in the office each week, and the required location is Beijing (Guomao).
Responsibilities
Own & deliver cross-team analytics epics end-to-end (often multi-quarter): scoping, design, implementation, rollout, and adoption, with minimal oversight
Set technical direction for our analytics/BI layer (Looker + dbt + Trino/Spark) and data products; lead design reviews and establish guardrails (cost, reliability, privacy, inclusion)
Model and govern data: design stable contracts (schemas/SLAs), manage lineage, and evolve domain models that unlock self-service and performance at scale.
Optimize performance & cost across engines (Trino, Spark/Databricks): plan-level analysis, join/partitioning strategies, aggregation layers, caching/materialization; set SLOs with monitoring/alerting.
Raise the bar on engineering quality: testing, CI/CD, documentation, privacy/security, on-call hygiene; lead incident reviews and drive permanent fixes.
Mentor & multiply: coach engineers/analysts, delegate effectively, and contribute to recruiting while holding the bar.
Qualifications
Education: Bachelor’s degree or higher in Computer Science or a related technical field, or equivalent practical experience.
Experience: 8–12+ years in data/analytics engineering or adjacent DE/BI roles, including 5+ years owning production semantic models & transformations and 3+ years leading cross-team initiatives end-to-end.
SQL & performance: Expert SQL with the ability to read/act on query plans (distributed + warehouse). Proven wins on TB-scale data (e.g., ≥2× latency reduction or ≥30% cost savings) via partitioning, file formats, pruning, aggregations, and caching/materialization
dbt at scale: Operated mid-to-large dbt projects (≈100+ models), using incremental models, tests, exposures, macros/packages, CI/CD, and data contracts; strong documentation and naming standards.
Looker semantic layer: Owned LookML modeling across multiple domains; shipped governed explores/measures for 100+ users, with version control, code review, release process, and change management that enable self-service analytics.
Engines & storage: Hands-on with Trino/Presto and/or Spark/Databricks (distributed plans, join strategies, partitioning, autoscaling); comfortable with Parquet/Iceberg table layouts and query-aware modeling.
Reliability & governance: You set SLOs for BI/analytics surfaces, establish monitoring/alerting, manage lineage & SLAs, and run post-incidents to land permanent fixes.
Leadership: Self-directed; sets technical direction for a domain, drives multi-quarter epics, mentors multiple engineers/analysts, leads design reviews, and raises the hiring/promo bar.
Software fundamentals: Proficient Python and data tooling; strong testing, CI/CD, code review hygiene; privacy/security awareness.
AI/LLM enablement: Experience designing or integrating AI-assisted analytics (e.g., chat-to-SQL over a semantic layer, RAG on dbt/Looker docs) with guardrails for access control/PII and an evaluation plan; can quantify adoption or ticket reduction.
Nice to Have
Ad-tech domain expertise (RTB auction dynamics, mediation, attribution, and LTV).
Production ops for analytics infra: GitOps (Argo CD), IaC (Terraform), Kubernetes-based data services; incident playbooks for data/BI.
Streaming & CDC: Kafka/Kinesis with Flink or Spark Structured Streaming to power near-real-time analytics.
JVM stack: Scala/Java for Spark jobs/UDFs or high-throughput data services.
Feature/ML data interfaces: feature marts or stores (e.g., Feast), batch/online syncing, model telemetry hooks.
Privacy & governance at scale: row/column-level security, tokenization, policy-as-code; familiarity with GDPR/CCPA impacts.
Data observability & lineage tooling: Datadog, Prometheus/Grafana, OpenLineage/DataHub/Amundsen; automated freshness/volume/uniqueness checks.
Experimentation: Experience building the foundations for A/B testing — event definitions, consistent metrics, and safeguards for valid results.
Multi-cloud / cross-region analytics deployments; Iceberg/Hudi table maintenance (compaction, vacuum, manifest tuning).
Location:
This is a hybrid (3 days/week in-office) role. Our Beijing office is located in Guomao.
Travel Expectations:
We offer several opportunities for in-person team gatherings, including but not limited to project meetings, regional meetups, and company-wide events. We expect our employees to attend these gatherings at least once per quarter. These gatherings provide essential opportunities for collaboration, communication, and team building.
This role is required to travel (domestically and internationally) a minimum of 1 week per quarter of planning meetings and other work events.
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