Senior Azure Data Engineer

2 weeks ago

Sydney, New South Wales, Australia INGRITY Full time $120,000 - $140,000 per year
Are you a well-rounded Senior Azure Data Engineer with a passion for architecting scalable, efficient, and secure data solutions?

Join our esteemed team at INGRITY, where your skills will play a crucial role in serving our clients effectively.
Location: Sydney.
Employment Type: Full-Time.
Eligibility: Open to candidates with full working rights in Australia.
About INGRITY:
INGRITY is a Microsoft Data and AI Solution Partner dedicated to delivering transformative data and AI-driven solutions. We collaborate with some of the best ASX-listed companies and medium-sized businesses, working closely with Microsoft to create measurable value for our customers. Our success is built on innovation, customer advocacy, and a strategic partnership with Microsoft.
About the job:

We're looking for a skilled professional who combines deep hands-on data engineering expertise with strong solution architecture and data modeling capabilities along with expert-level SQL and strong Python skills to design and deliver impactful data platforms on Azure.

Key Responsibilities:
  • Collaborate with stakeholders to gather requirements and define project goals.
  • Design and implement end-to-end data solutions on Azure, including data ingestion, transformation, modeling, storage, and analytics.
  • Develop robust ETL/ELT pipelines using SQL and Python, ensuring scalability, efficiency, and quality.
  • Architect data solutions leveraging Azure Data Factory, Azure Synapse Analytics, Azure Databricks, Azure SQL Database, and Data Lake Storage.
  • Build and maintain efficient data models (star/snowflake schema) to support reporting and analytics.
  • Create architecture diagrams, technical documentation, and implementation plans.
  • Set up and manage CI/CD pipelines and deployment automation using Azure DevOps or similar tools.
  • Apply cloud best practices using the Well-Architected Framework (WAF) and Cloud Adoption Framework (CAF).
  • Implement data security, governance, and monitoring using Azure-native tools. Provide technical leadership and support across the entire project lifecycle.
Required Skills & Experience:
  • 5 years of experience in Azure Data Engineering or solution architecture.
  • Hands-on experience with key Azure services including ADF (Azure Data Factory), Azure Databricks, Synapse, Azure SQL Database, and Data Lake Storage.
  • SQL Expert - Advanced experience in writing, optimizing, and troubleshooting complex SQL queries, stored procedures, and database performance.
  • Strong Python Skills - Proficient in Python for data manipulation, transformation, and scripting within data engineering pipelines.
  • Strong background in data modeling for analytics and reporting, including dimensional modeling techniques.
  • Experience designing ETL/ELT workflows, ingesting data from structured/unstructured sources (CSV, JSON, Parquet, APIs).
  • Familiarity with CI/CD processes, Git, pull requests, and release management.
  • Knowledge of Azure security best practices, including IAM, encryption, and role-based access.
  • Proficiency in monitoring tools like Azure Monitor, Log Analytics, and Databricks utilities.
  • Strong problem-solving skills, attention to detail, and ability to work independently.
  • Experience with PySpark or Spark in Databricks.
  • Exposure to real-time data processing tools (e.g., Event Hub, Stream Analytics).
  • Knowledge of data cataloging and governance tools (e.g., Azure Purview).
  • Familiarity with DevOps practices in data environments.
About INGRITY:

We are a fast-growing, progressive Sydney-based data & analytics company. We service many large corporates and SMBs in the financial services sector, to derive value from data. At Ingrity, we are committed to building a diverse and inclusive workforce to deliver value to customers. As a value-driven organisation, we nurture and support our people by having a laser-sharp focus on skill and talent development, collaboration, and flexibility.

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