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 RoleDeliver 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.
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.
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).
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.
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.
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.)
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 TechnicalExpert 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).
Solid grounding in statistics and experimental thinking (hypothesis testing, bias/variance intuition, model evaluation).
Ability to choose the simplest appropriate approach and explain why.
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.
Experience with data orchestration tools (e.g., Airflow, Prefect) and CI/CD for data.
Familiarity with analytics engineering practices documentation, testing, metric governance, and semantic layers.
Experience representing the organization in industry initiatives or communities as a data practitioner.
Stakeholders consistently use your outputs to make decisions (clear metrics, trusted dashboards, reliable datasets).
The assets you develop are stable, tested, documented, and easy for others to extend.
You reduce cycle time for answering business questions by standardizing datasets and automating repeatable analyses.
You are known for solving problems with the simplest effective approach, while keeping quality and governance high.
How we’ll support you
Training and development to help you excel in your career
Coaching and support from experts in your team
A culture of continuous learning to aid progression
A range of flexible benefits that you can tailor to suit your needs
About us and our teams
Please visit our company website for further information:
https://www.db.com/company/company.html
We at DWS are committed to creating a diverse and inclusive workplace, one that embraces dialogue and diverse views, and treats everyone fairly to drive a high-performance culture. The value we create for our clients and investors is based on our ability to bring together various perspectives from all over the world and from different backgrounds. It is our experience that teams perform better and deliver improved outcomes when they are able to incorporate a wide range of perspectives. We call this #ConnectingTheDots.
Skills Required
- Expert SQL (optimized queries, dimensional modeling, performance tuning)
- Expert Python and Pandas for data wrangling and reproducible analysis
- Hands-on experience with Snowflake and/or BigQuery (warehouse concepts, ELT patterns)
- Experience building models and transformations with dbt (modeling, tests, documentation, version control)
- Experience designing dashboards and narratives in Qlik or equivalent BI tools (Power BI, Tableau, Looker)
- Solid grounding in statistics, experimental thinking, and model evaluation
- Strong stakeholder management, communication, and consulting mindset (clarify objectives, drive alignment)
- Designing and maintaining data quality checks, lineage, SLAs, and documentation for trusted datasets
- Experience with data orchestration tools (Airflow, Prefect) and CI/CD for data
- Familiarity with analytics engineering practices: testing, metric governance, semantic layers
- Experience representing the organization in industry initiatives or communities as a data practitioner
What We Do
DWS Group (DWS) with EUR 933bn of assets under management (as of 30 June 2024) aspires to be one of the world's leading asset managers. Building on more than 60 years of experience, it has a reputation for excellence in Germany, Europe, the Americas and Asia. DWS is recognized by clients globally as a trusted source for integrated investment solutions, stability and innovation across a full spectrum of investment disciplines. We offer individuals and institutions access to our strong investment capabilities across all major liquid and illiquid asset classes as well as solutions aligned to growth trends. Our diverse expertise in Active, Passive and Alternatives asset management – as well as our deep environmental, social and governance focus – complement each other when creating targeted solutions for our clients. Our expertise and on-the-ground knowledge of our economists, research analysts and investment professionals are brought together in one consistent global CIO View, giving strategic guidance to our investment approach. DWS wants to innovate and shape the future of investing. We understand that, both as a corporate as well as a trusted advisor to our clients, we have a crucial role in helping to navigate the transition to a more sustainable future. With approximately 4,500 employees in offices all over the world, we are local while being one global team. We are committed to acting on behalf of our clients and investing with their best interests at heart so that they can reach their financial goals, no matter what the future holds. With our entrepreneurial, collaborative spirit, we work every day to deliver outstanding investment results, in both good and challenging times to build the best foundation for our clients’ financial future.
