Technical data analyst

Skills are highlighted, but primarily Python, SQL, AWS and some visualization experience like tableau Qualifications8+ years of experience in Data Analytics, Data Engineering, or Applied Data ScienceExpert proficiency in SQL for complex queries, data modeling, and transformation logicStrong experience with data pipelines and orchestration toolsHands-on experience with big data ecosystems (e.g., Snowflake, Databricks, Spark, AWS/GCP/Azure data services)Advanced skills with Tableau, Power BI, or Looker, including data source optimization and dashboard automationProficiency in Python or R for scripting, data analysis, and automationFamiliarity with APIs, RESTful data integration, and Git-based workflowsExperience with A/B testing frameworks, statistical analysis, or experimentation design preferredKnowledge of Customer Success or Support Operations KPIs (e.g., resolution rate, contact deflection, AHT) preferredStrong problem-solving, collaboration, and communication skills with both technical and non-technical audiencesA proactive, growth-oriented mindset, passionate about improving data accessibility and scalability Key ResponsibilitiesData Engineering & Pipeline Development: Design, build, and maintain ETL/ELT data pipelines to consolidate and transform customer success data from multiple international sources. Collaborate with Data Engineering and Platform teams to optimize data ingestion, model performance, and processing efficiency. Implement data validation, version control, and automated quality checks to ensure accuracy and reliability. Partner with engineering to establish data governance, metric definitions, and consistent schema designs across global regions.Dashboard Development & Automation: Develop and maintain scalable, self-service dashboards and data products that empower teams to explore key metrics (e.g., tNPS, AHT, repeat contacts, digital adoption). Leverage tools such as Tableau or Qlik for visualization, ensuring optimal performance and intuitive design. Automate recurring reports and KPI tracking workflows, integrating them with modern BI and data orchestration tools.Advanced Analytics & Insights: Conduct deep-dive analyses and root-cause investigations to identify patterns, trends, and optimization opportunities across global support operations. Use SQL, Python, or R to build analytical scripts, support experimentation frameworks, and perform ad-hoc statistical analyses. Partner with Data Science teams to productionize analytical models or pipelines supporting A/B testing and performance measurement.Tooling & Capability Enablement: Evaluate, implement, and integrate new data tools, APIs, and cloud-native platforms to expand analytic capabilities. Champion best practices in data architecture, version control (Git), and continuous integration/deployment (CI/CD) for analytics workflows. Serve as a technical point of contact for data platform enhancements supporting International Customer Success analytics.Communication & Collaboration: Translate complex analytical outputs into clear, actionable insights for technical and business partners. Collaborate cross-functionally with engineering, product, and operations to embed data-driven decision-making into strategy. Support a data-first culture through documentation, training, and process standardization.