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Senior Data Architect Position

2 weeks ago

Sydney, New South Wales, Australia beBeeData Full time $180,000 - $210,000
Senior Data Architect Job

Job Description:

The role of a Senior Data Architect involves the design and implementation of scalable solutions using data engineering best practices. This position requires expertise in building real-time data pipelines, implementing data mesh architectures, and enhancing client data interactions.

Key Responsibilities:

  • Data Engineering: Design and architect scalable data solutions using Databricks Lakehouse Architecture, applying best practices around Delta Lake, data modelling, and distributed processing.
  • Developing Data Solutions: Implement and enhance data-driven solutions integrating with clients' systems using state-of-the-art tools such as Databricks, Snowflake, Google Cloud, and AWS.
  • Data Pipeline Development: Develop and optimise high-performance, batch, and real-time data pipelines employing advanced streaming technologies like Kafka and Flink.
  • Database and Storage Optimisation: Optimise and manage a broad array of database technologies from traditional relational databases to modern NoSQL solutions.
  • Big Data Processing & Analytics: Utilise big data frameworks such as Apache Spark and Apache Flink to address challenges associated with large-scale data processing and analysis.
  • Cloud Data Management: Implement and oversee cloud-specific data services including AWS Redshift, S3, Google BigQuery, and Google Cloud Storage.
  • Security and Compliance: Ensure all data practices comply with security policies and regulations, embedding security by design in the data infrastructure.

Required Skills and Qualifications:

  • Databricks Platform: Hands-on experience in Databricks to help build scalable, high-performance data solutions.
  • Core Data Engineering Tools & Technologies: Proficiency in SQL and Spark, familiarity with platforms such as Databricks and Snowflake.
  • Data Storage Expertise: Knowledgeable in data warehousing technologies like BigQuery, Snowflake, and Databricks, proficient in managing various data storage formats.
  • Building and Managing Large-scale Data Systems: Experienced in developing and overseeing large-scale data pipelines and data-intensive applications within production environments.
  • Data Modelling Expertise: Proficient in data modelling, understanding the implications and trade-offs of various methodologies and approaches.
  • Infrastructure Configuration for Data Systems: Competent in setting up data system infrastructures favouring infrastructure-as-code practices.
  • Programming Languages: Proficient in Python and SQL, with additional experience in programming languages like Java, Scala, GoLang, and Rust considered advantageous.
  • CI/CD Implementation: Knowledgeable about continuous integration and continuous deployment practices using tools like GitHub Actions and ArgoCD.
  • Testing Tools and Frameworks: Experienced with data quality and testing frameworks such as DBT, Great Expectations, and Soda.
  • Commercial Application of Data Engineering Expertise: Demonstrated experience in applying data engineering skills across various industries and organisations in a commercial context.
  • Consulting and Advisory Skills: Ex experienced in a consultancy or professional services setting, offering expert advice and crafting customised solutions that address client needs.

Professional Experience and Qualifications:

  • Professional Experience: At least 8+ years of data engineering or equivalent experience in a commercial, enterprise, or start-up environment. Consulting experience within a technology consultancy or professional services firm is highly beneficial.
  • Educational Background: Degree or equivalent experience in computer science or a related field.