Machine Learning Operations Engineer

Role Summary:

The role will entail managing a team of data engineers responsible for implementing machine learning models and creating highly resilient data pipelines. It will also entail leading various projects including migration activities and POCs.

Job Description:

• Manage big data initiatives for the Data Science and AI Group while working closely with the technology team, vendors, and consultants.
• Coordinate with business and technology stakeholders to ensure that requirements are implemented as expected.
• Facilitate implementation of analytical tools.
• Recommend and maintain the data model that will support business intelligence, advanced analytics, and campaign management initiatives.
• Implement enhancements in the existing MLOps pipeline.
• Establish data quality checks to identify integrity issues and report findings to technology management for resolutions.
• Establish and implement data governance guidelines to ensure delivery of high quality and secure data to meet regulatory requirements and promote efficiency and revenue growth.
• Establish a catalog of commonly used data fields to aid data users in exploring the banks data, improve the understanding of data across the bank, and promote consistency of KPI definition.
• Conduct best practice research to continuously improve current data management and analytic processes and ensure data management and analytics tools/processes are at par with global standards.
• Conduct POC on new solutions.

Soft Skills Required: 

• Leadership: Experience in mentoring and coaching team members, fostering a culture of growth and improvement, and proactively learning new tools, technologies, and best practices to drive team success.
• Strong Communication & Collaboration: Proven ability to build relationships across functions, communicate effectively with both business and technical stakeholders, and manage conflicts through proactive stakeholder engagement.
• Analytical & Strategic Thinking: Demonstrates sound decision-making by involving the right stakeholders, understanding interdependencies, and planning accordingly with a big-picture mindset.

Technical Skills Required: 

• Proficient in data processing tools (SQL, Python, R).
• Strong knowledge of batch and stream data processing techniques (Spark, Kafka).
• Strong knowledge in MLOps and related concepts.
• Experienced in data modelling and data warehousing techniques.
• Experienced in using Git for version control and CI/CD.
• Experienced in using business intelligence tools (Power BI, Tableau).
• Preferably with experience in Cloudera and Snowflake.
• Preferably with experience in the banking and financial services industry.