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 modern 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 robust 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