About Us:
Infosys is a global leader in next-generation digital services and consulting. We enable clients in more than 56 countries to navigate their digital transformation. With over four decades of experience in managing the systems and workings of global enterprises, we expertly steer our clients through their digital journey. We do it by enabling the enterprise with an AI-powered core that helps prioritize the execution of change. We also empower the business with agile digital at scale to deliver unprecedented levels of performance and customer delight. Our always-on learning agenda drives their continuous improvement through building and transferring digital skills, expertise, and ideas from our innovation ecosystem.
Visit www.infosys.com to see how Infosys (NYSE: INFY) can help your enterprise navigate your next.
Location: Sydney, Australia
Infosys is seeking a highly skilled and motivated Senior Associate Consultant to join our DNA team. As a Senior Associate Consultant/Senior Data Engineer, you will be responsible for designing, building, and maintaining scalable data pipelines, systems and infrastructure to support data-driven initiatives. You will collaborate closely with cross-functional teams to ensure the availability, reliability, and performance of our client’s data systems and solutions.
Salary Range: 90243- 98861 AUD Annual Gross
Please Note:- The above salary range is only indicative and maybe subject to suitable enhancements, based on internal company processes

Responsibilities:

• Implement, and optimize end-to-end data pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data.
• Develop robust ETL (Extract, Transform, Load) processes to integrate data from diverse sources into target data ecosystem.
• Implement data validation and quality checks to ensure accuracy and consistency.
• Understand data modeling principles and data architecture design to maintain data models, schemas, and database structures to support analytical and operational use cases
• Automate data workflows such as data ingestion, aggregation, and ETL processing
• Optimize data storage and retrieval mechanisms for performance and scalability.
• Evaluate and implement data storage solutions, including relational databases, NoSQL databases, data lakes, and cloud storage services.
• Build and maintain integrations with internal and external data sources
• Monitor system performance, troubleshoot issues
• Implement optimizations to enhance reliability and efficiency.
• Implement data security controls and access management policies to protect sensitive information
• Document technical designs, workflows, and best practices to facilitate knowledge sharing and maintain system documentation.
• Identify and resolve complex data-related issues.
• Write clean, efficient, and well-documented code.
• Provide technical guidance and support to team members and stakeholders as needed.

Requirements:

• Bachelor’s degree in computer science, Engineering, Information Systems, or related field. Master’s degree preferred.
• Proven experience in data engineering, software development, or related roles.
• Proficiency in programming languages commonly used in data engineering (e.g., Python, Java, Scala).
• Strong knowledge of database systems, data modeling techniques
• SQL proficiency and knowledge of various database technologies
• Proficiency with ETL tools used in data engineering (e.g., SSIS, Databricks, Azure Data Factory).
• Experience with big data technologies and frameworks such as Hadoop, Spark, Kafka
• Experience with cloud platforms and services such as AWS, Azure, GCP
• Experience working with cloud Data Warehouse solutions such as Snowflake, Redshift, BigQuery
• Experience working with data ingestion tools such as Fivetran, stitch, or Matillion
• Knowledge in scripting languages like Bash.
• Experience in data pipeline and workflow management tools such as Airflow, Azkaban
• Experience in data visualization tools like Tableau and Power BI
• Good problem solving and troubleshooting skills.
• Effective communication and collaboration skills in a team-oriented environment.
• Ability to adapt to evolving technologies and business requirements
• Experience with Agile development methodology and ALM tools Jira, Rally, Confluence, ServiceNow.
• Experience working with a distributed version control and source code management tool such as Git.
• Experience with software engineering practices including CI/CD

Nice to have:

• Zeal to contribute in production support space and enhancements through code fixes where necessary.
• Flexibility in multitasking and able to contribute beyond squad specific allocation.
• Able to bring initiatives and innovations that help simplify processes and automate monotonous tasks.
• Experience working with Connect-Direct and Ctrl-m Scheduler, Elastic Search (ELK Stack), Splunk

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.
At Infosys, we recognize that everyone has individual requirements. If you are a person with disability, illness or injury and require adjustments to the recruitment and selection process, please contact our Recruitment team for adjustment only on Infosys_ta@infosys.com or include your preferred method of communication in email and someone will be in touch.
Please note in order to protect the interest of all parties involved in the recruitment process, Infosys does not accept any unsolicited resumes from third party vendors. In the absence of a signed agreement any submission will be deemed as non-binding and Infosys explicitly reserves the right to pursue and hire the submitted profile. All recruitment activity must be coordinated through the Talent Acquisition department.

• Implement, and optimize end-to-end data pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data.
• Develop robust ETL (Extract, Transform, Load) processes to integrate data from diverse sources into target data ecosystem.
• Implement data validation and quality checks to ensure accuracy and consistency.
• Understand data modeling principles and data architecture design to maintain data models, schemas, and database structures to support analytical and operational use cases
• Automate data workflows such as data ingestion, aggregation, and ETL processing
• Optimize data storage and retrieval mechanisms for performance and scalability.
• Evaluate and implement data storage solutions, including relational databases, NoSQL databases, data lakes, and cloud storage services.
• Build and maintain integrations with internal and external data sources
• Monitor system performance, troubleshoot issues
• Implement optimizations to enhance reliability and efficiency.
• Implement data security controls and access management policies to protect sensitive information
• Document technical designs, workflows, and best practices to facilitate knowledge sharing and maintain system documentation.
• Identify and resolve complex data-related issues.
• Write clean, efficient, and well-documented code.
• Provide technical guidance and support to team members and stakeholders as needed