- Build a greenfield real-time event-streaming platform using Kafka, Flink, and ClickHouse for segmentation and targeting at scale
- Set the technical direction for the platform as products scale
- Own warehouse datasets and transformations end to end using BigQuery, dbt, SQL, and Python
- Establish data quality, testing, and observability standards across streaming and batch
- Tune systems for cost and latency as data volumes scale
- Raise engineering standards through code review, mentoring, and reusable team patterns
- Incorporate AI into personal workflows and the platform, including coding agents and automated quality checks
- Support Immutable Audience's data lifecycle from ingestion and transformation through modelling and delivery to internal and external customers

Requirements

- Strong, hands‑on data engineering fundamentals: pipelines, modelling, correctness, scale, and failure modes
- Strong SQL and Python, used daily in production
- Experience designing, building, and scaling production data pipelines end to end
- Depth in the modern batch stack: dbt, an orchestrator, and a cloud warehouse
- Data modelling skills for analytics and product use cases at scale
- Experience with event data at billions‑of‑rows scale
- Experience with AWS or GCP and infrastructure-as-code
- Data quality and observability mindset, including testing, monitoring, and alerting
- Track record of setting technical direction through architecture decisions and mentoring senior engineers




- Ability to explain data trade-offs to non-technical stakeholders
- Strong ownership and pragmatic judgment
- Comfort with ambiguity and shifting priorities
- Bonus: production streaming systems experience with Kafka and Flink, ideally with ClickHouse
- Bonus: gaming, adtech, or martech event-data background
- Bonus: using AI to multiply output and improve team AI fluency

Core Competencies

Demonstrates expertise in building and scaling real-time event-streaming platforms using Kafka, Flink, and ClickHouse, while ensuring data quality and observability. Proficient in SQL and Python for data engineering, with a solid focus on data lifecycle management and technical direction.

Highest-signal resume keywords

- Kafka Event Streaming
- Flink Stream Processing
- SQL Data Engineering
- Python Programming
- Data Quality and Observability

ATS Optimization Keywords

Hard Skills

- Data Pipeline Design
- Data Modelling
- BigQuery
- Dbt
- Cloud Warehouse
- Event Data Management
- Infrastructure-as-Code
- Testing and Monitoring
- Data Transformation
- Cost and Latency Optimization

Soft Skills

- Ownership
- Pragmatic Judgment
- Communication with Non-Technical Stakeholders
- Mentoring
- Comfort with Ambiguity

Industry Keywords

- Gaming
- Adtech
- Martech
- Event Data
- Data Lifecycle

Tools & Technologies

- AWS
- GCP
- ClickHouse
- Orchestrator
- AI Integration

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📌 Lead Data Engineer (Australia)
🏢 Jobtailor
📍 Australia