The core responsibilities for the job include the following :

Data Warehousing and Modelling :

- Own the design of silver/gold layer tables: grain, join keys, schema, partitioning, and clustering decisions for large-scale datasets.

- Build and maintain batch and streaming ETL pipelines (using big data technologies) feeding the medallion architecture.

- Design idempotent, observable pipelines with QC gates, backfill strategies, and clear failure semantics.

- Standardize data models and conventions across multiple game products/projects.

- Deliver high-quality data pipelines with utmost importance to the correctness and availability of the data to different target audiences/systems.

Design Decisions and Technical Ownership :

- Lead source-system profiling and drive grain, deduplication, and enrichment decisions backed by data, not assumptions.

- Own trade-off calls: cost vs. freshness, load strategy, schema evolution, and query/slot optimization.

- Document designs and decisions so they survive beyond the author.

Stakeholder Management :

- Partner with analysts, PMs, and game teams to translate business questions into warehouse requirements and reconcile instrumentation gaps.

- Communicate design proposals and data profiling findings to both technical and leadership audiences.

Requirements :

- Experience: 5+ years in data engineering, with deep data warehousing exposure at scale.

- SQL and BigQuery: Expert-level SQL; hands-on BigQuery optimization (partitioning, clustering, slots, cost).

- Data Modelling: Strong command of medallion architecture, dimensional/fact modelling, grain definition, and schema design.

- Programming: Production-grade Python; PySpark on Dataproc (or Databricks/EMR equivalents).

- Streaming: Working experience with Kafka-based ingestion (Confluent preferred).

- Data Quality: Experience building validation, observability, and reconciliation into pipelines.

- Stakeholder Skills: Proven ability to gather requirements, challenge assumptions, and communicate trade-offs clearly.

- Education: Bachelor's/Master's in CS, engineering, or a related field (or equivalent experience).

AI-Augmented Engineering (Must-Have) :

- We expect AI tooling to be part of your daily workflow, with sound judgment on when to trust and when to verify:

1. Hands-on daily use of AI coding agents Claude Code / CLI, Claude web app, or equivalents (Cursor, Copilot, Gemini CLI).

2. Experience creating reusable AI assets: custom skills/commands, prompt libraries, or repo-level agent context.

3. Familiarity with MCP or similar integrations connecting agents to warehouses and internal tools.

4. Ability to demonstrate real examples of AI-accelerated work and where you drew the human-review line.

Preferred:

- LLM-in-pipeline experience: AI-driven data quality checks, anomaly triage, or text-to-SQL for self-service analytics.

- Broader GCP: Pub/Sub, Cloud Run, Cloud Functions, Cloud SQL.

- Gaming, fintech, or other high-volume consumer event domains.