Manager of Data Analytics

The Data Analytics domain for HF is identified as the customer-facing product field encompassing all types of analytics reporting that are utilized to extract practical insights from data, inform data-driven decision-making, and drive the success of the customer's business. This product is offered to our customers across all HF platforms (HF, iHR, Ento and Thrive). As the Data Analytics Manager, you will be tasked with leading the data analytics team composed of data scientists, data engineers, and software engineers, focusing on data architecture, product delivery, roadmap, scale, security, and performance. This position collaborates with the product team to define the data analytics roadmap and assists in fulfilling this roadmap. Responsibilities Team Leadership: - Build, develop, and lead a team of data scientists, data engineers, and software engineers. - Set clear team goals and expectations, providing coaching and mentorship to assist team members in improving and excelling in their roles. - Promote a collaborative and innovative team culture that encourages sharing insights and best practices. Data Analytics Technical Strategy: - Work together with R&D management to define the organization's data analytics strategy and objectives. - Develop and execute a roadmap to achieve these objectives, including aspects of data architecture, processing, analysis, and reporting. Data Analysis, Interpretation, and Data Visualization: - Supervise the process of data collection, cleaning, and analysis, identifying trends, patterns, and insights, and creating tools for our customers to visualize this. - Utilize statistical and machine learning techniques to extract actionable insights and recommendations. - Align data analysis with our customers' business goals and objectives. - Create and communicate clear and engaging data visualizations and reports that present insights to our customers. - Use data visualization tools and platforms to display information in a format that non-technical team members can understand. Data Quality Assurance: - Implement and maintain data quality standards and procedures to ensure the accuracy and reliability of data used for analysis. - Work together with data engineers to enhance data collection and storage processes. Stay Current with Industry Trends: - Keep updated with the latest developments in data analytics, machine learning, Gen AI, and data science. - Identify and evaluate emerging technologies and methodologies that could enhance data analytics capabilities. Compliance and Data Security: - Ensure all data handling and analysis activities comply with data privacy and security regulations. - Collaborate with cybersecurity, legal, and compliance teams to ensure all data-related practices align with relevant laws and regulations. Performance Evaluation: - Regularly assess the analytics team's performance, provide feedback, and identify opportunities for improvement. - Measure and report on the impact of data analytics initiatives. Continuous Improvement: - Implement process improvements and best practices to enhance the efficiency and effectiveness of the data analytics function. - Encourage a culture of continuous learning and development within the team. Communication and Presentation: - Effectively communicate complex data insights and findings to both technical and non-technical stakeholders. - Present analysis results in a compelling and persuasive manner to influence decision-making. Requirements Qualifications and experience: - Relevant tertiary qualification in a Data, IT, or business-related degree, or experience and training in relevant previous roles. - 3+ years of experience in analyzing/manipulating data and building statistical models in an analyst or data scientist role. - 3+ years of leadership experience in a data team. - Proficiency in SQL - Experience using statistical languages (e.g., R, Python) to manipulate data and draw insights. - Experience with data visualization tools including embedded reporting/visualizations in SaaS software (e.g., Tableau) - Experience building machine learning models. Desirable: - Experience with Gen AI methodology Technical skills: - Reporting and presentation skills - Data wrangling skills for reporting, dashboarding, and machine learning - Enjoy working with data and mathematics. - Enterprising, innovative, and strategic thinking - Excellent problem-solving skills. - Strong communication skills (oral and written) - Strong interpersonal skills - Keen attention to detail and logical thinking - Ability to work effectively with people and within teams. - Excellent organizational skills - Enthusiasm for lifelong learning - Ability to work under pressure and within deadlines. - Good commercial knowledge - Ability to explain complex ideas to those with limited technology or data understanding. - Experience working in Data warehouse Design, methodologies Extraction, Transformation and Loading, Data Analysis, Data migration, Data preparation, Graphical Presentation, Statistical Analysis, Reporting, Validation, and Documentation. - Exposure to OLAP, OLTP, data warehouse development, Data Lake, and lake house architectures. - Extensive experience working with ETL processes. - Extensive experience working with data visualization tools and embedded reporting. - Experience in troubleshooting, performance tuning, and resolving issues. - Experience using AGILE Methodology and/or SCRUM Process. Benefits: - Salary is approximately 200K, which may include bonuses. - Hybrid working model. - Locations: Sydney, Melbourne, Brisbane.