Advanced Data Engineer

7 days ago

Melbourne, Victoria, Australia beBeeDataEngineer Full time $160,000 - $200,000
About Data Engineering Roles

We're seeking a skilled professional to drive data pipeline efficiency, scalability, and reliability.

This position focuses on automating, monitoring, and optimizing data workflows, ensuring seamless data delivery across the organization.

The ideal candidate will have expertise in data engineering best practices, CI/CD automation, cloud infrastructure, observability, and security.

  • Design and implement CI/CD pipelines for automated data workflow deployment.
  • Develop infrastructure-as-code (IaC) templates for provisioning cloud-based data environments/resources.
  • Automate ETL/ELT workflow management using orchestration tools like Apache Airflow, Prefect, or AWS Step Functions.
  • Optimize data ingestion, transformation, and processing pipelines for scalability and efficiency.
Data Pipeline Automation & Orchestration

Our team is responsible for streamlining data pipeline deployment through continuous integration and continuous deployment.

We ensure observability, performance, and security in cloud data environments by implementing real-time monitoring dashboards and alerts.

Automating infrastructure management and data workflow orchestration enables us to deliver high-quality data insights to stakeholders.

We drive best practices for data governance, cost optimization, and data quality assurance.

Cloud Infrastructure & Security

We manage data infrastructure in AWS, Azure, or GCP, ensuring high availability and security.

Our team implements role-based access control (RBAC), encryption, and data security best practices to protect sensitive information.

We collaborate with security teams to ensure compliance with industry regulations and standards.

Monitoring cloud costs and optimizing resource usage for storage, compute, and networking is essential to our success.

Monitoring, Observability & Incident Response

We develop real-time monitoring dashboards and alerts for data pipeline performance and failures.

Using observability tools like Datadog, Prometheus, Grafana, and CloudWatch, we track latency, errors, and throughput.

Establishing incident response workflows and automated rollback strategies for failures ensures minimal downtime and maximum system uptime.

Our team works closely with Site Reliability Engineers (SRE) and DevOps teams to maintain high system resilience.

DataOps & DevOps Best Practices

We implement version control, automated testing, and release management for data pipelines.

Promoting DataOps methodologies improves collaboration between data engineering, IT, and business teams.

Ensuring proper data governance, metadata tracking, and auditability in data workflows is crucial to our success.

Optimizing data processing efficiency drives business value and customer satisfaction.

Collaboration & Stakeholder Engagement

We work closely with data engineers, data scientists, and platform engineers to improve data workflows.

Acting as a DataOps champion, we provide guidance on best practices for pipeline reliability and security.

Supporting business teams ensures data availability, accessibility, and lineage tracking.

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