Job Description
As a Data Analyst at BitGo, you will play a pivotal role in transforming raw data into actionable business insights that drive strategic decision-making. This position requires a deep understanding of data analytics principles and their application to product development, business growth, and operational efficiency. You will collaborate with cross-functional teams across engineering, product management, and customer success to identify opportunities, design experiments, and refine product strategies. The role also involves developing and maintaining standardized metrics and dashboards to monitor key performance indicators (KPIs) and uncover trends that inform business hypotheses. Your work will directly impact the scalability and reliability of BitGo's data infrastructure, ensuring that data pipelines deliver accurate, timely, and actionable information to stakeholders.
Key Responsibilities
- Develop and deliver data-driven business insights by analyzing user behavior, market trends, and operational metrics to identify growth opportunities and product optimization strategies.
- Design, implement, and standardize metrics and dashboards that provide real-time visibility into product performance, customer engagement, and business outcomes.
- Collaborate with cross-functional teams, including product managers, engineers, and customer success, to translate data findings into actionable recommendations and solutions.
- Build and automate reporting systems for KPIs across BitGo's diverse product and service offerings, ensuring scalability and consistency in data delivery.
- Partner with product managers to design and execute A/B tests, validate hypotheses, and refine product ideas based on empirical data.
- Work with engineering teams to improve the availability, integrity, accuracy, and reliability of data pipelines, ensuring seamless integration with business tools and platforms.
- Act as a data evangelist by educating stakeholders on data-driven decision-making, fostering a culture of analytics within the organization, and aligning data initiatives with business goals.
- Conduct root-cause analysis on data anomalies and performance bottlenecks to ensure data quality and operational efficiency across all systems.
- Develop predictive models and scenario analyses to forecast business outcomes and support long-term strategic planning.
- Collaborate with marketing and sales teams to analyze customer segmentation, campaign effectiveness, and revenue trends to inform growth strategies.
Job Requirements
- Proven experience in data analysis, preferably in a SaaS or fintech environment, with a focus on product analytics, business intelligence, or growth hacking.
- Advanced proficiency in SQL, Python, or R for data manipulation, analysis, and visualization, with experience in tools like Tableau, Power BI, or Looker.
- Strong understanding of data pipeline architecture, including ETL processes, cloud data platforms (e.g., Snowflake, BigQuery), and data warehousing best practices.
- Excellent communication skills to translate complex data findings into clear, actionable insights for non-technical stakeholders and cross-functional teams.
- Ability to work independently and collaboratively in a fast-paced, dynamic environment with tight deadlines and evolving priorities.
- Experience with agile methodologies and a track record of delivering data-driven projects on time and within scope.
- Knowledge of machine learning techniques and their application to business problems, with experience in model deployment and monitoring.
- Strong analytical mindset with the ability to identify patterns, correlations, and anomalies in large datasets to inform strategic decisions.
- Experience with customer analytics frameworks, including cohort analysis, funnel analysis, and lifetime value (LTV) modeling.
- Excellent problem-solving skills and the ability to think critically about data challenges, proposing innovative solutions to improve data quality and business outcomes.
- Ability to work with stakeholders at all levels, from executives to engineers, to align data initiatives with organizational goals.
- Experience with data governance practices, including data quality standards, metadata management, and compliance with regulatory requirements.
- Strong attention to detail and a commitment to accuracy in data analysis and reporting processes.
- Ability to develop and maintain scalable data systems that support business growth and operational efficiency.
- Experience with data storytelling techniques to present findings in a compelling and actionable manner to decision-makers.
