About the role
Hive Health is looking for a curious, capable data team member looking to grow their data journey with us in the intersection of business intelligence, data science, and data engineering. As a Data Analyst, you will help build the data and analytics backbone of Hive Health in order to provide an unparalleled healthcare experience for Filipinos. As one of the pioneering members of the team, you will work closely with other functions to deliver exceptional data and analytics solutions to drive decisions, improve processes, and deliver great user experiences.
Location
This is a hybrid role based in Metro Manila, Philippines, subject to change based on business needs. While most work may be accomplished remotely, you must be able to commute to our office in the Ortigas area at least once per week, as needed.
Responsibilities
In this role, you'll be expected to:
- Develop reliable data products, analyses, and technical solutions that support business decision-making. This includes contributing to data analysis, dashboard development, data pipelines, automation, experimentation, and AI/ML initiatives under the guidance of senior team members.
- Collect, process, validate, and manage datasets while maintaining high standards of data quality, governance, and documentation. Build and support scalable data workflows that are traceable, reproducible, and compliant with organizational data standards.
- Collaborate with cross-functional stakeholders to understand business problems, translate requirements into technical solutions, and deliver high-quality work within agreed timelines.
- Continuously learn and apply modern data technologies, engineering practices, and analytical techniques while building a strong foundation across the end-to-end data lifecycle.
This role will be a great fit if:
- You're an early-career software or data professional who enjoys solving technical problems and wants to build a strong foundation across analytics, data engineering, and AI & data science before choosing a specialization. Whether you're a fresh graduate with relevant coursework or projects, an early-career data professional, or transitioning into the field through self-learning or bootcamps, you'll have opportunities to explore multiple areas of modern data work.
- You're curious, adaptable, and enjoy learning new technologies. You thrive in fast-paced environments, take ownership of your work, and are willing to step outside your immediate responsibilities to help solve problems and continuously improve how the team operates.
Qualifications & Skills
Must-have:
- Bachelor's degree in Computer Science, Information Systems, Information Technology, Engineering, Mathematics, Statistics, or a related quantitative field; or equivalent practical experience.
- Strong programming fundamentals, preferably in Python.
- Working knowledge of relational databases and proficiency in SQL.
- Understanding of fundamental data concepts, including data modeling, data transformation, data quality, and basic statistical analysis.
- Familiarity with data visualization principles and experience using spreadsheets or business intelligence tools to communicate insights.
- Basic familiarity with version control (e.g., Git) and collaborative development practices.
- Strong analytical thinking and problem-solving skills, with the ability to break down complex business problems into structured technical solutions.
- Excellent written and verbal communication skills, with the ability to collaborate effectively across technical and business teams.
- Demonstrated curiosity, adaptability, and a strong willingness to continuously learn new technologies, tools, and domains.
Nice-to-have:
- Experience with business intelligence and data visualization tools (e.g., Power BI, Looker, Tableau).
- Exposure to cloud data platforms such as Google Cloud Platform (e.g., BigQuery), AWS, or Azure.
- Familiarity with modern data engineering and analytics engineering concepts, including ETL/ELT pipelines, dbt, APIs, data transformation, or workflow orchestration.
- Exposure to AI and machine learning concepts, including predictive modeling, generative AI, large language models (LLMs), prompt engineering, retrieval-augmented generation (RAG), or AI-assisted application development.
- Experience working with non-relational databases, large datasets, or external APIs.
- Familiarity with collaborative development practices and modern tooling, such as Git, testing, Docker, or CI/CD.
