7+ Years in Data Engineering | 10+ Years in Consulting with Databrick lakehouse
Role Overview
We are seeking an experienced Senior FDE (Resident Solution Architect) to lead
enterprise data engineering and analytics initiatives using the Databricks Lakehouse
Platform. The ideal candidate will possess deep expertise in designing scalable data
solutions, architecting cloud-native platforms, and delivering high-performance
distributed data processing systems.
As a Resident Solution Architect, you will work closely with customers to define
technical strategy, provide architectural guidance, optimize data platforms, and
ensure successful delivery of enterprise-scale data modernization programs.
Key Responsibilities
- Design and implement scalable, secure, and high-performance data engineering
solutions on the Databricks Lakehouse Platform.
- Lead end-to-end architecture, design, and implementation of enterprise data
platforms.
- Provide technical leadership and architectural guidance to customer and
internal engineering teams.
- Develop and optimize distributed data processing pipelines using Apache Spark.
- Collaborate with stakeholders to understand business requirements and
translate them into scalable technical solutions.
- Drive cloud-native data platform implementations across AWS, Azure, or GCP.
- Implement CI/CD pipelines and DevOps best practices for production
deployments.
- Apply performance tuning and scalability optimization techniques to improve
platform efficiency.
- Work closely with Data Scientists and ML teams to support MLOps
implementation where required.
- Stay current with Databricks platform capabilities and recommend best
practices for enterprise adoption.
Required Qualifications
- 7+ years of experience in Data Engineering, Data Platforms, and Analytics.
- 10+ years of overall consulting experience.
- Databricks Data Engineering Professional Certification (mandatory).
- Completion of the required Databricks training and certification courses.
- Proven experience delivering 6–8+ Databricks implementation projects with
hands-on development responsibilities.
- Strong expertise in distributed computing using Apache Spark, including Spark
architecture and runtime internals.
- Hands-on experience with Databricks platform features and ecosystem.
- Strong understanding of CI/CD practices for production-grade deployments.
- Working knowledge of MLOps concepts and implementation.
- Experience in performance tuning and scalability optimization of large-scale
data workloads.
Cloud Expertise
- Robust working knowledge of at least two major cloud platforms:
o Microsoft Azure
o Amazon Web Services (AWS)
o Google Cloud Platform (GCP)
- Deep implementation expertise in at least one cloud platform.
Preferred Skills
- Enterprise solution architecture
- Data Lakehouse architecture
- Cloud-native data platform design
- Performance optimization
- Data pipeline modernization
- Stakeholder management
- Technical consulting and customer engagement
Why Join Us?
- Work on large-scale enterprise data modernization initiatives.
- Collaborate with industry-leading architects and engineering teams.
- Drive innovation using the latest Databricks and cloud technologies.
- Influence strategic technology decisions for global customers.
Pay: ₹635,079.19 - ₹2,683,894.55 per year
Benefits:
- Provident Fund
- Work from home
Application Question(s):
- What is your current location?
- What is your current ctc?
- What is your salary expectation?
- What is your notice period?
Experience:
- Total: 8 years (Preferred)
- Data lake: 7 years (Preferred)
- Data Engineering: 7 years (Preferred)
- Database design: 6 years (Preferred)
- Apache spark : 6 years (Preferred)
- Azure: 6 years (Preferred)
- AWS: 6 years (Preferred)
- Google Cloud Platform: 6 years (Preferred)