Data Analyst

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Overview: As a Data Analyst, you will be instrumental in driving the advanced data analytics strategy for Global Operations, delivering breakthrough insights that support the BBG and Carlsbad sites. Your focus will be on simplifying and automating reporting and visualization, constantly seeking ways to enhance efficiency. Collaborating with domain experts, you’ll query and synthesize diverse data sources to answer key business questions. As part of a multidisciplinary team of Data Engineers, Data Scientists, and Business Analysts, you will generate data-driven insights that enhance business profitability. You are strategic and methodical, skilled at translating complex data into clear narratives and actionable recommendations, and an effective communicator who excels in applying advanced data analytics practices. Responsibilities: Gain a deep understanding of Global Operations technology and data structures to support key business objectives. Integrate complex and diverse data sets to provide deeper insights into company operations, collaborating cross-functionally as needed. Identify opportunities for data acquisition, develop and implement data collection systems, and create integrated data pipelines to strategically guide production teams and support quality improvement initiatives. Support the development and execution of data analytics and data science use cases to drive actionable insights. Develop and automate processes for data access and processing to ensure efficient utilization of relevant information. Promote a culture of high-quality analytics by setting standards for dashboard design, comprehensive documentation, and proactive stakeholder communication. Demonstrate a passion for advancing the company's analytics strategy through innovative and effective solutions. Communicate complex ideas clearly and effectively, adhering to best practices in software engineering. Perform other duties as assigned by management to support business goals. What you will do: Clarify requirements, propose iterative solutions, and communicate findings to both technical and non-technical audiences. Collaborate with cross-functional stakeholders to define, document, and govern core business metrics, ensuring alignment and consistency in all reporting. Define business logic and requirements for data pipelines, working alongside data engineers to ensure a reliable and efficient foundation for analytics. Proactively identify key questions and deliver insights that influence strategic decisions. Design, develop, and manage the lifecycle of key business intelligence dashboards, ensuring they deliver accurate, actionable, and timely insights for decision-making. Manage the lifecycle of existing analytical tools, performing ongoing maintenance, troubleshooting, and iterative enhancements based on stakeholder feedback and changing priorities.
Requirements
Qualification: Bachelor's Degree in Engineering, Mathematics, Data Science, Statistics, or Computer Science and 2 years of directly related experience required Or Master's degree in Engineering, Mathematics, Data Science, Statistics, or Computer Science and 1 years of directly related experience preferred. Basic Requirements: Strong proficiency in Python for data manipulation and analysis, with hands-on experience using libraries like pandas and NumPy. Advanced proficiency in SQL for querying and manipulating complex, large-scale datasets. Proven experience with data parsing, building automation scripts, and working with programmatic APIs. Familiarity with diverse and heterogeneous data structures, including JSON, XML, images, and Google Sheets. Experience with cloud data platforms such as Snowflake, GCP, and AWS. Comfort working with the command line interface. Preferred Qualifications: Familiarity with various database structures and schemas, including relational and non-relational databases. Experience building simple data applications or interactive dashboards using frontend solutions like Streamlit or R Shiny. Familiarity with version control systems like Git and CI/CD principles. Experience applying advanced analytic methodologies such as Machine Learning, optimization, or simulation. Experience in the life sciences, healthcare, or diagnostics industry is preferred.