
Sr. Data Analyst
2 weeks ago
Job Title: Data Engineer
Location: Sydney, New South Wales, Australia
We are seeking a skilled Data Engineer with experience in data engineering, particularly with Apache Py Spark, SQL, and data migration processes.
The ideal candidate will design, implement, and maintain data pipelines and ensure the integrity and availability of data across large systems.
Responsibilities
Design, develop, and maintain scalable data pipelines using Apache Py Spark and SQL.
Collaborate with cross functional teams to support data migration projects and ensure data quality.
Utilize Azure Databricks and Teradata/Cloudera for data processing and analytics.
Implement Dev Ops practices to automate data workflows and improve deployment processes.
Monitor and optimize data systems for performance and reliability.
Document data engineering processes and maintain clear communication with stakeholders.
Work independently and as part of a team to meet project deadlines.
Mandatory Skills
3 to 5 years of experience in data engineering with a focus on Apache Py Spark and SQL.
Proficiency in Azure Databricks and experience with Teradata or Cloudera.
Hands-on experience with data migration projects.
Strong understanding of large systems architecture and data flow.
Excellent oral and written communication skills.
Self-motivated with the ability to work under tight deadlines.
Preferred Skills
Familiarity with Dev Ops practices and tools.
Experience with Python for data manipulation and analysis.
Knowledge of data warehousing concepts and best practices.
Experience working with cloud-based data solutions.
Qualifications
Bachelor's degree in Computer Science, Information Technology, or a related field.
Relevant certifications in data engineering or cloud technologies are a plus.
If you are passionate about data engineering and eager to contribute to innovative projects, we encourage you to apply and join our team in Sydney
Tech Mahindra is an equal opportunity employer.
We value diversity and inclusivity in our workforce.
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