Lead Data Engineer

Company: Go Digital Technology

Website: Visit Website

Business Type: Small/Medium Business

Company Type: Service

Business Model: B2B

Funding Stage: Private Equity

Industry: Information Technology

Salary Range: ₹ 25-40 Lacs PA

Job Description Lead Data Engineer Mumbai | 🏢 Hybrid (4 Days Office, 1 Day Remote)

About The Role We are looking for a Lead Data Engineer to drive the design and delivery of scalable, enterprise-grade data platforms . This role is ideal for someone who can own end-to-end data architecture, lead engineering teams, and influence data strategy across the organization.

Key Responsibilities

• Lead the design and implementation of end-to-end data engineering solutions
• Architect scalable, fault-tolerant data pipelines on distributed systems
• Define and enforce data engineering best practices, standards, and frameworks
• Work closely with business stakeholders to translate requirements into robust technical solutions
• Drive data modelling, pipeline optimization, and performance tuning initiatives
• Ensure data quality, security, and compliance standards are met
• Collaborate with enterprise architects and leadership on data strategy and roadmap
• Mentor and lead a team of data engineers, ensuring delivery excellence

Skills Required

• Expert-level proficiency in Python, PySpark, and Advanced SQL
• Strong experience with Big Data technologies (Hadoop, Hive)
• Deep understanding of Spark internals (DAG, partitioning, memory management, optimization)
• Extensive experience in ETL/ELT frameworks and data modelling
• Strong hands-on experience with AWS cloud and data services
• Experience with CI/CD pipelines, Git, and Agile delivery models
• Ability to design scalable and modular data architectures

Experience

• 7–10 years of experience in Data Engineering
• Proven experience in leading teams and delivering large-scale data platforms
• Strong track record of solution architecture and stakeholder management

Nice to Have

• Experience with real-time / streaming data pipelines
• Hands-on with workflow orchestration tools (Autosys, etc.)
• Exposure to enterprise data governance and architecture frameworks