Data Science Team Leader

18 hours ago

Shimoga, Karnataka, India beBeeAnalytics Full time ₹ 2,05,60,000 - ₹ 2,35,70,000

Key to delivering a seamless user experience is developing robust analytics systems. As an Analytics Engineering & Automation Lead, you will play a pivotal role in this endeavor.

The ideal candidate will have expertise in designing and scaling datamarts, dashboards, business rules, and ML deployment pipelines. This includes owning process automation, SLAs, and governance to ensure a reliable, future-ready analytics ecosystem.

We are seeking an exceptional individual who can execute and lead a team of analysts/engineers. Key responsibilities include:

1. Analytical Engineering
  • Design and oversee datamarts and curated data views across domains.
  • Establish data modeling best practices, documentation, and governance.
  • Mentor team members on SQL and data engineering patterns.
2. Dashboarding & Visualisation
  • Drive dashboard standards, governance, and usability across business teams.
  • Lead the design of executive-level and operational dashboards.
  • Manage a backlog of dashboard requests with clear SLAs.
3. Business Rule Engines (BREs)
  • Own the design and governance of production rule engines for credit/fraud policies.
  • Ensure scalable, tested, and version-controlled deployment of rules.
  • Partner with Risk/Policy/Product teams for agile implementation.
4. ML Deployment & Maintenance
  • Lead the deployment and monitoring of batch-based ML models.
  • Define standards for model execution, monitoring, and retraining workflows.
  • Collaborate with model builders for smooth model-to-production handoff.
5. Automation & SLAs
  • Define and enforce SLAs for data, dashboard, and BRE changes.
  • Introduce automated solutions using tools like GenAI, n8n, dbt, CI/CD.
  • Continuously improve efficiency of analytics delivery.
6. Team Leadership
  • Lead a small team (2–4 analysts/engineers) with responsibility for prioritization, quality, and development.
  • Conduct regular reviews, mentorship, and skill development.
  • Act as the bridge between business stakeholders and the analytics team.

Qualifications

- 5–8 years of experience in analytics engineering / BI / data platforms.

- Advanced SQL, data modeling, and pipeline orchestration experience.

- Strong BI expertise (Tableau, PowerBI, Looker, or equivalent).

- Hands-on with ML model deployment (batch pipelines, monitoring, scheduling).

- Familiarity with cloud platforms (BigQuery, Snowflake, GCP/AWS/Azure).

- Experience with workflow automation (Airflow, dbt, n8n, CI/CD).

- Prior experience in leading small teams and stakeholder management.

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