Experienced Senior Cloud Data Engineer

2 weeks ago

Tiruchi, Tamil Nadu, India beBeeDataEngineering Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Job Overview

We are seeking an experienced Senior Data Engineering Lead with a strong focus on Databricks and cloud-based data engineering to lead our data engineering team.

This leadership role requires a visionary who can design, develop, and manage scalable data infrastructure and pipelines, while mentoring and inspiring a team of data engineers.

The successful candidate will work closely with cross-functional teams including data scientists, analysts, and software engineers to enable robust data-driven decision-making and support business goals.

Key Responsibilities:

  • Mentorship and Team Management: Lead and manage a team of data engineers, providing technical guidance, fostering collaboration, and promoting innovation.
  • Data Infrastructure Design: Architect, design, and oversee implementation of large-scale data pipelines, data lakes, and cloud-based data warehouses using Databricks, Apache Spark, and Snowflake.
  • ETL/ELT Workflow Development: Develop and optimize ETL/ELT workflows ensuring performance, reliability, and scalability of data infrastructure.
  • Cross-Functional Collaboration: Collaborate with business stakeholders, data scientists, and software teams to understand requirements and translate them into scalable, efficient data solutions.
  • Best Practices Implementation: Implement best practices for data quality, governance, security, and compliance.
  • Innovation and Improvement: Drive continuous improvement of data engineering processes, standards, and tools across the organization.
  • Presentation and Communication: Support presales activities by contributing to RFPs, technical proposals, and client engagements.
  • Tech Advancement: Stay abreast of emerging data technologies and trends, recommending innovative solutions to enhance analytics capabilities.
  • Resource Planning and Project Delivery: Manage resource planning, project prioritization, and delivery timelines ensuring alignment with business objectives.
  • Professional Development: Lead performance reviews, identify skill gaps, and champion professional development within the data engineering team.
  • Process Optimization: Facilitate cross-team communication to streamline data workflows and improve overall delivery.

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