Analyst Product Operations - Multiple Teams

• Develop Data Pipeline by Designing, building, and optimizing ETL/ELT workflows using tools like Databricks, SQL, Python or pyspark
• Develop and maintain robust, scalable, and efficient data pipelines for processing large datasets from source to emerging data.
• Support senior team members in identifying and implementing AI/ML-driven automation for repetitive data tasks such as data validation, response coding, and anomaly detection
• Assist in building scheduled data pipelines using Databricks workflows for automated data refresh and processing
• Develop and maintain client-ready visualization dashboards in Tableau specifically, with some support from senior team members
• Support data preparation and analysis using tools such as Alteryx or SQL
• Assist in the execution of benchmarking surveys, including developing sample specifications, and managing research data
• Provide ongoing support for subscription customers, including generating custom data cuts, answering methodology questions, and preparing insights for delivery
• Collaborate with team members to ensure high-quality, timely outputs across product operations workstreams
• Translate technical concepts and data into clear and structured insights for internal and external stakeholders with support from senior team members
• Contribute to developing reusable templates, process documentation, and standard work practices that improve efficiency and scalability
• Demonstrate curiosity and hypothesis-driven thinking to uncover patterns, and build robust analytical models which can help solve problems at hand

Required Qualifications, Experience & Skills  

• Education
  • Must Have: Graduate from a top-tier institute or equivalent engineering colleges / universities.
  • Good To Have: Degree in a quantitative discipline such as Statistics, Mathematics, Engineering, Computer Science, Econometrics, Business Analytics, or Market Research
• Experience
  • Must Have: 1 to 4 years of professional experience in Python or Pyspark and SQL and Databricks at renowned companies. The ideal candidate will have strong expertise in Python or Pyspark and SQL with proven experience working on Databricks.
  • Good To Have: Basic experience of Tableau, with building dashboards and maintain reports
  • Good To Have: Hands-on exposure to survey design, data processing, and visualization is preferred.
  • Good To Have: Professional experience in Alteryx

• Technical Skills
  • 1 to 4 years of professional experience in Python or Pyspark, SQL and Databricks at renowned companies. The ideal candidate will have strong expertise in Python or Pyspark and SQL with proven experience working on Databricks.
  • Basic experience of Tableau, with building dashboards and maintain reports
  • Proven experience using Alteryx or similar data prep/transformation tools
  • Understanding of core statistical and analytical methods
  • A solid understanding of ETL tools like Python as well as basic knowledge of DevOps practices and CI/CD pipelines, will be Good to Have.
  • Good to have - Basic familiarity with AI/ML concepts and their practical application in automating data workflows (e.g., data quality checks, monitoring or insight generation)
  • Good to have- Exposure to Databricks workflows or similar orchestration tools for scheduling and automating data pipelines, and integrating Databricks with downstream reporting tools like Tableau

• Other Skills
  • Strong analytical, organizational and problem-solving skills with attention to detail
  • Experience in developing clear, audience-appropriate presentations and deliverables
  • Excellent interpersonal and communication skills, both written and verbal
  • Collaborative team player who thrives in a fast-paced, evolving environment
  • Proven self-starter (comfortable with what could be a remote team) wit demonstrated problem solving and organizational skills
• • Eager to learn and open to feedback, with a growth-oriented mindset