Data Engineering Lead

1 day ago

Kolkata, West Bengal, India beBeeData Full time ₹ 1,20,00,000 - ₹ 2,50,00,000
Job Description

We seek an experienced Data Engineering Lead who can execute and lead a team of analysts/engineers. This role will design and scale data platforms, dashboards, business rules, and ML deployment pipelines, as well as own process automation, SLAs, and governance for a reliable analytics ecosystem.

This position is responsible for overseeing the development and maintenance of data-driven solutions, ensuring scalability, reliability, and performance. The ideal candidate will have strong technical skills in data engineering, as well as excellent leadership and communication skills.

  • Data Engineering
    • Design and oversee data platforms and curated data views across domains.
    • Establish data modeling best practices, documentation, and governance.
    • Mentor team members on SQL and data engineering patterns.
  • Dashboards & Visualization
    • 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.
  • 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.
  • 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.
  • 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.
  • 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.

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