Job Description
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
Robust 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 strong 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

📌 Lead Data Engineer (New South Wales)
🏢 Jobtailor
📍 New South Wales