Analytics Analyst, AS

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Job Description: Job Title: Analytics Analyst, AS Location: Bangalore, India Role Description We are looking for a Data Scientist / Data Engineer who combines strong analytical depth with a consulting mindset : you listen first, clarify the business problem, and then deliver the easiest workable solution not the most technical one. You will partner with stakeholders to define data requirements, build reliable datasets and pipelines, develop models and statistical analyses where appropriate, and turn outcomes into clear, decision-ready insights through modern BI/visualization tools. You are an expert in SQL and Python (Pandas) and highly capable with Snowflake, BigQuery, dbt, Qlik , and other data focused frameworks and visualization platforms. You care about data quality, repeatability, and transparency, and you communicate trade-offs balancing speed, risk, and long-term maintainability. The role aligns closely with "analytics engineering" practices bridging data engineering and analytics with strong communication and documentation. What we'll offer you As part of our flexible scheme, here are just some of the benefits that you'll enjoy Best in class leave policy Gender neutral parental leaves 100% reimbursement under childcare assistance benefit (gender neutral) Sponsorship for Industry relevant certifications and education Employee Assistance Program for you and your family members Comprehensive Hospitalization Insurance for you and your dependents Accident and Term life Insurance Complementary Health screening for 35 yrs. and above Your key responsibilities Purpose of the Role Deliver timely analytics, statistical modeling, and data products that address current and future business needs. Translate ambiguous questions into measurable hypotheses, reliable data assets, and actionable insights focusing on impact over complexity . Build and maintain scalable, well-governed datasets and transformations to enable self-service analytics and consistent reporting. 1) Business Problem Framing (Consulting Mindset) Partner with business and technology stakeholders to clarify objectives , success metrics, constraints, and decision points. Drive structured discovery: identify the simplest dataset/model/visualization that answers the question with acceptable confidence. Provide clear recommendations, trade-offs (time/cost/risk), and "next best actions," not just charts or code. 2) Data Requirements & Data Product Delivery Define data requirements end-to-end: sources, definitions, lineage, refresh cadence, SLAs, and data quality expectations. Design and implement robust pipelines (batch/ELT as appropriate) and curated data models using dbt and modern cloud warehouses (e.g., Snowflake, BigQuery ). Apply best practices for performance and maintainability (e.g., warehouse-optimized modeling/partitioning/denormalization where relevant). 3) Data Preparation, Quality, and Reliability Perform data collection, processing, cleaning, and validation to ensure accuracy, completeness, and consistency. Implement automated quality checks, documentation, and monitoring so stakeholders can trust the numbers. 4) Analytics, Modeling, and Research Examine and identify patterns and trends to answer business questions and improve decision-making. Build statistical reports and analytical methodologies; where data science is the focus: Create/maintain modeling approaches, data mining architectures, and robust evaluation methodologies. Research and apply relevant data science principles and emerging techniques to business problems. At higher levels, contribute to or lead research initiatives to advance analytics capabilities. 5) Visualization, Storytelling, and Enablement Build intuitive and accurate dashboards and narratives using Qlik and other BI/visualization tools (e.g., Power BI, Tableau, Looker). Present insights in business language highlighting drivers, uncertainty, and implications. Enable self-service: publish reusable datasets, metrics, and "single source of truth" definitions. (Example of Python-driven data processing with visualization in Qlik is a known pattern.) 6) Efficiency & Automation Identify and implement opportunities to increase efficiency via automation (repeatable pipelines, templated analyses, reusable notebooks, shared semantic layers). Prefer pragmatic solutions (e.g., a well-modeled table + simple dashboard) over complex systems unless complexity is clearly justified. Your skills and experience Core Technical Expert SQL : writing optimized queries, dimensional modeling concepts, debugging data issues, performance tuning. Expert Python + Pandas : data wrangling, reproducible analysis, packaging reusable components. Strong hands-on experience with: Snowflake and/or BigQuery (warehouse concepts, performance/cost awareness, ELT patterns). dbt (modeling, tests, documentation, version control workflows). Qlik and other BI/visualization tools (dashboard design, user adoption, semantic consistency). Analytics

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