Senior Data Engineer – Sydney, Australia
Salary: AUD 108,571 – 119,893 Annual Gross. Flexible working arrangements (hybrid, reviewed case‐by‐case basis).
Responsibilities
* Enhance, optimise, and maintain existing data ingestion, transformation, and extraction pipelines and assets built for reporting and analytics on the Unified Data platform.
* Work with the Product Owner and Chapter Lead to understand quarterly priorities and OKRs, and gather detailed requirements from initiative owners or program sponsors based on planned Epics.
* Perform data wrangling, profiling, and analysis for new datasets ingested from source systems and those derived from existing datasets using on‐premises and cloud‐native tools.
* Coordinate with other teams for planning, design, governance, engineering, and release management to ensure timely and accurate delivery of data and services.
* Build new data assets and pipelines tailored to downstream requirements (e.g., datasets for Tableau vs. TM1 cubes).
* Document low‐level design, source‐to‐column mapping, test cases, production release implementation plans, and operational support manuals.
* Schedule data pipelines using tools like Control M and Airflow, ensuring correct upstream dependencies and SLA compliance.
* Provide warranty support to Operations post‐production release and update documentation accordingly.
* Collaborate with multiple business and IT teams to deliver the final outcome.
* Ensure effective communication and collaboration across multiple stakeholders and business units of Optus.
* Gain functional knowledge by reviewing the existing code base to better understand the Optus domain.
* Adapt to an agile environment with constant change and diverse challenges in a competitive market.
* Work effectively within tight timeframes.
* Balance the needs of multiple stakeholders across various business initiatives and manage competing priorities to ensure timely resolution.
* Develop sensible alternate solutions, utilising market knowledge while working quickly and accurately to meet short deadlines and manage competing priorities.
Skill / Competencies / Experience
ESSENTIAL
* Education: Bachelor's, Master's, or Doctorate degree in Mathematics, Statistics, Computer Science, or Information Management.
* Work Experience: 8+ years of experience in Data Engineering and Data Warehousing.
* Technical Skills:
o Design, build and optimise scalable pipelines using Azure Databricks, PySpark, and Delta Lake for large‐scale data transformation.
o Develop complex SQL and Python code for data transformation and validation across large datasets in cloud data platforms.
o Strong hands‐on experience with Azure Databricks (notebooks, workflows, cluster configuration), Delta Lake, and Azure data services (ADLS, ADF).
o Proficiency in Python, PySpark, CI/CD, and SQL for production‐grade data engineering solutions.
o Familiar with CI/CD processes including BitBucket/GitHub, Jenkins and Nexus.
o Capable of managing both structured and unstructured data types.
PREFERRED
* Non‐technical skills: Experience working with a team of data engineers across different locations and time zones.
* Excellent communication skills.
* Strong prioritisation abilities.
* Pragmatic stakeholder management.
ADDITIONAL
* Excellent customer‐facing skills.
* Excellent written and verbal communication skills.
* Strong attention to detail and outstanding analytical and problem‐solving skills.
* Proficient in Excel, Word and PowerPoint.
* Ability to prioritise and manage multiple concurrent activities.
Benefits
* Income Protection Insurance
* Paid Parental and Volunteer leaves
* Employee Assistance Program (EAP)
* Flexible working arrangements (hybrid, reviewed case‐by‐case basis)
* Health Insurance Discount and Well‐being Program
* Access to Fitness and Gym Memberships
* Salary packaging and novated leasing
All aspects of employment at Infosys are based on merit, competence and performance. We are committed to embracing diversity and creating an inclusive environment for all employees. Infosys is proud to be an equal opportunity employer.
#J-18808-Ljbffr