Lead Data Engineer
H.B. Fuller (company site, opens in a new tab)10+ yrsChennaiOn-siteDirect career pageEst. 30–48 LPA
Posted 5h ago
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Reach people at H.B. Fuller
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at H.B. Fuller you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Own and evolve an enterprise data platform ensuring 99.9%+ reliability and high performance for analytics and data-driven products.
- Architect and implement scalable, distributed data platforms supporting batch and real-time processing with emphasis on DataOps advancement.
- Lead and mentor a team of engineers, define engineering standards, and collaborate with business leaders to translate complex needs into technical solutions.
Minimum Requirements
- Bachelor’s degree in Computer Science, Information Systems, Data Engineering, Analytics, Statistics, or related field.
- 10+ years of data engineering experience inclusive of leadership responsibilities.
- Proven experience delivering enterprise-scale data platforms with strong stakeholder engagement.
- Expertise in Snowflake and Microsoft Fabric platforms, plus experience with orchestration tools like Matillion or Azure Data Factory.
Ideal Candidate Profile
- Experienced in modern hybrid, multi-engine data architectures including lakehouse and warehouse paradigms with hands-on performance tuning and cost optimization.
- Demonstrated ability to lead technical teams and establish data governance, security, and documentation standards compliant with regulations like GDPR.
- Comfortable working closely with senior stakeholders and cross-functional teams to deliver scalable, metadata-driven data solutions with focus on reliability and efficiency.