Analytics Analyst, AS (Karnataka)

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. 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 / Data Science - Solid grounding in statistics and experimental thinking (hypothesis testing, bias/variance intuition, model evaluation). - Ability to choose the simplest appropriate approach and explain why. Qualified / Consulting Behaviors - Strong stakeholder management: clarify what decision are we supporting and drive alignment on definitions. - Crisp .