Data Lead

Data Lead

About the role :

As a Data Lead at NxtWave, you will own analytics for a business unit end to end, acting as its analytics partner and point of accountability. You will turn vague business questions into well-framed analytics problems, design the data models and architecture that answer them, and deliver insights the business unit head can act on.

You will lead and mentor a team of analysts, set the direction for self-serve analytics, and bring AI into everyday analytics workflows. The role sits at the intersection of business, analytics engineering and data governance.

Roles and responsibilities :

Business ownership and problem framing

Own analytics for a defined part of the business unit and be accountable for its metrics, insights and outcomes

Convert vague or open-ended business problems into structured analytics problems: design experiments, derive clear questions, hypotheses, metrics, data needs and success criteria.

Partner continuously with the business unit head, bringing proactive insights and clear recommendations, not just reports

Analyze trends and root causes, define or improve KPIs, and explain the "why" behind movements in the numbers statistically as needed.

Data architecture and modeling

Contribute to data architecture decisions on the warehouse, including data modelling, optimisations, historical data maintenance, etc

Design, develop, and maintain dbt models. Implement unit and custom data quality tests, and maintain comprehensive documentation.

Define and maintain semantic models and metric definitions to ensure consistent definitions, and reporting across the analytics platform.

Work with engineering on ETL/ELT pipelines, source data contracts and data collection improvements

Self-serve analytics, automation and AI

Guide stakeholders in AI Powered self-serve analytics. Support stakeholders to adapt the AI tools like Hex, Claude, etc to answer data questions independently, enabling fast, decentralized decision-making.

Use AI tools in analytics workflows (EDAs, query generation, documentations, etc) and help the team adopt them responsibly

Automate recurring reports and data workflows to reduce manual effort.

Write Python scripts for data processing, automation or analysis, and deploy them where needed (scheduled jobs, cloud functions or similar)

Team leadership and mentoring

Lead and mentor a team of analysts: Break large initiatives into deliverables for the team and track them to completion

Monitor the team's output for accuracy, quality and timeliness

Build the team's skills in AI powered analytics, business thinking and communication

Take ownership of team performance, outcomes and continuous improvement

Data quality, governance and documentation

Ensure data quality, access control and governance in line with organizational guidelines

Recommend improvements to data collection and promote strong governance practices

Stay current with tools, technologies and best practices in analytics and the modern data stack

Skills required :

1.Advanced SQL: optimized queries, window functions, CTEs and handling large datasets

2.Hands-on experience with at least one cloud data warehouse: BigQuery (GCP), Snowflake or Redshift

3.Strong grasp of data modeling concepts: dimensional modeling (star/snowflake schemas), facts and dimensions, slowly changing dimensions, grain and normalization trade-offs

4.Experience with dbt: models, sources, tests, macros, documentation and layered project structure

5.Understanding of semantic layers and metric definitions

6.Fair understanding of the data ecosystem: ETL/ELT patterns, orchestration, ingestion tools and the modern data stack

7.Strong experience with visualization tools such as Looker Studio, Power BI or Tableau

8.Python for analysis and automation (pandas, API calls, scheduled scripts), with the ability to deploy scripts when needed

9.Practical use of AI tools in analytics work and good judgment on where they help and where they need review

10.Ability to define business metrics and identify the right data points for a problem

11.Good understanding of statistics: distributions, sampling, hypothesis testing, correlation vs causation

12.Strong data storytelling and written communication: clear reports, dashboards and recommendations

13.Experience automating data workflows and enabling self-serve analytics

14.Proven ability to plan and deliver projects end to end, independently and on time

15.Mentorship and people skills to guide, review and grow junior analysts

Attention to detail with a logical, structured approach to problem-solving

Good to have

Knowledge of data science and applied statistics (A/B testing, regression, forecasting)

Hands-on machine learning experience: building, evaluating or deploying models (e.g., lead scoring, propensity, churn)

Exposure to orchestration tools (Airflow, Cloud Composer, dbt Cloud) and version control with Git

Experience with warehouse cost monitoring and optimization

Prior experience in EdTech, consumer or sales-funnel analytics

Experience, location and working days

Experience: 4-6 years in analytics, with hands-on data modeling and some experience leading or mentoring analysts

Language: Proficiency in English

Location: Hyderabad

Working days: 5-day week