Data Scientist / Data Engineer – “Pragmatic Consultant, AVP

Job description

## **Job Description:** **Job Title: Data Scientist / Data Engineer – “Pragmatic Consultant, AVP** **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 / 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. ### **Professional / Consulting Behaviors** - Strong stakeholder management: clarify “what decision are we supporting?” and drive alignment on definitions. - Crisp communication: translate data into implications, options, and recommendations. - Ownership and pragmatism: deliver incremental value early; iterate with feedback. ### **Nice to Have** - Experience with data orchestration tools (e.g., Airflow, Prefect) and CI/CD for data. - Familiarity with anal