Job Description

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

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- Strong, hands‐on data engineering fundamentals: pipelines, modelling, correctness, scale, and failure modes
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- Strong SQL and Python, used daily in production
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- Experience designing, building, and scaling production data pipelines end to end
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- Depth in the up-to-date batch stack: dbt, an orchestrator, and a cloud warehouse
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- Data modelling skills for analytics and product use cases at scale
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- Experience with event data at billions‐of‐rows scale
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- Experience with AWS or GCP and infrastructure-as-code
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- Data quality and observability mindset, including testing, monitoring, and alerting
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- Track record of setting technical direction through architecture decisions and mentoring senior engineers
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- Ability to explain data trade-offs to non-technical stakeholders
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- Strong ownership and pragmatic judgment
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- Comfort with ambiguity and shifting priorities
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- Bonus: production streaming systems experience with Kafka and Flink, ideally with ClickHouse
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- Bonus: gaming, adtech, or martech event-data background
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- Bonus: using AI to multiply output and improve team AI fluency
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Core Competencies

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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 strong focus on data lifecycle management and technical direction.

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Highest-signal resume keywords

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

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Hard Skills

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

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

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- Gaming
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- Adtech
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- Martech
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- Event Data
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- Data Lifecycle
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Tools & Technologies

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- AWS
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- GCP
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- ClickHouse
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- Orchestrator
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- AI Integration
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📌 Lead Data Engineer (New South Wales)
🏢 Jobtailor
📍 New South Wales