Senior GCP Big Data Engineer – responsible for designing, building, and maintaining large-scale data pipelines and ensuring data governance and performance on Google Cloud Platform.
Responsibilities
GCP BigData Engineering (BigQuery + Dataform + Pub/Sub)
Expert in designing and optimising BigQuery schemas, partitioning/clustering, cost/performance tuning, query optimisation, and policy tag integration.
Building streaming and batch pipelines using Apache Beam/Dataflow and Pub/Sub with exactly-once semantics, backpressure handling, and replay strategies.
Strong experience with Dataform (or similar) for SQL-based transformations, dependency graphs, unit tests, and multi-environment deployments.
Production-grade Python for ETL/ELT, distributed processing, robust error handling, and testable modular design.
Designing resilient Airflow DAGs on Cloud Composer: dependency management, retries, SLAs, sensors, service accounts, and secrets.
Monitoring, alerting, and Cloud Logging/Stackdriver integration for end-to-end pipeline observability.
Data Security & Governance on GCP
Hands-on with Dataplex (asset management, data quality, lineage), BigQuery policy tags, Cloud IAM (least privilege, fine-grained access), KMS (key rotation, envelope encryption), and audit trails via Cloud Logging.
Practical experience implementing PII controls (data masking, tokenisation, attribute-based access control) and privacy-by-design in pipelines.
Tech Stack & Focus Areas
Cloud Run & APIs: Building stateless microservices for data access/serving layers, implementing REST/gRPC endpoints, authentication/authorisation, rate limiting.
Data Modelling: Telecom-centric event models (e.g., CDRs, network telemetry, session/flow data), star/snowflake schemas, and lakehouse best practices.
Performance Engineering: BigQuery slot management, materialised views, BI Engine, partition pruning, cache strategies.
Secure Source Manager (CI/CD): Pipeline-as-code, automated tests, artifact versioning, environment promotion, canary releases, and GitOps patterns.
Data Quality & Testing: Great Expectations/Deequ-like checks, schema contracts, anomaly detection, and automated data validations in CI/CD.
Streaming Patterns: Exactly-once delivery, idempotent sinks, watermarking, late data handling, windowing strategies.
Observability & SRE Practices: Metrics, logs, traces, runbooks, SLIs/SLOs for data platforms, major incident response to support DevOps.
Cost Governance: BigQuery cost controls, slot commitments/reservations, workload management, storage lifecycle policies.
Domain Knowledge (Mobile Networks): Familiarity with 3G/4G/5G network data, OSS/BSS integrations, network KPIs, and typical analytics use cases.
Experience Level
• 7+ years total in data engineering; 4+ years on GCP with production systems.
• Evidence of impact: led end-to-end delivery of large-scale pipelines (batch + streaming) with strict PII governance; owned performance/cost optimisation initiatives in BigQuery/Dataflow at scale; implemented CI/CD for data workflows; drove operational excellence (SLAs, incident management, RTO/RPO awareness, DR patterns).
• Soft skills: technical leadership, code reviews, mentoring, clear documentation, cross-functional collaboration with Network/Analytics teams, and a bias for automation & reliability.
Employment type
Contract
Location
Sydney, New South Wales, Australia
Salary
A$140,****** – A$180,******
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