About LetitbexAI:
LetitbexAI is a fast-growing AI-driven technology company focused on building intelligent, scalable, and enterprise-grade solutions. We work at the intersection of AI, data engineering, cloud, and business transformation, helping organizations unlock real value from artificial intelligence.
Position: Senior Field Data Engineer (FDE)
Experience: 10+ Years
Notice Period: Can be considered up 15 Days
Job Title: Senior Field Data Engineer (Resident Solutions Architect)
Experience: 10+ Years (7+ Years in Data Engineering, Data Platforms & Analytics)
Job Summary
We are looking for an experienced Senior Field Data Engineer (Resident Solutions Architect) with strong expertise in the Databricks Data Intelligence Platform. The ideal candidate will have extensive hands-on experience designing, developing, and optimizing enterprise-scale data platforms while working closely with customers to deliver scalable, high-performance data and AI solutions.
Key Responsibilities
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Design, build, and optimize scalable data engineering solutions using the Databricks Platform.
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Collaborate with customer stakeholders to understand business requirements and translate them into technical solutions.
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Develop and optimize Spark-based ETL/ELT pipelines for large-scale data processing.
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Architect and implement data solutions across AWS, Azure, and/or GCP cloud platforms.
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Provide technical leadership and best practices for Databricks implementations.
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Optimize workloads for performance, scalability, and cost efficiency.
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Implement CI/CD pipelines and deployment automation for production workloads.
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Support MLOps workflows and collaborate with data science teams for production AI/ML deployments.
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Troubleshoot complex technical issues and provide architectural guidance.
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Stay updated with the latest Databricks platform capabilities and recommend adoption where appropriate.
Required Qualifications
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10+ years of consulting experience with 7+ years in Data Engineering, Data Platforms, and Analytics.
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Databricks Data Engineering Professional Certification completed, along with the required Databricks training.
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Successfully delivered 6–8+ Databricks implementation projects with hands-on development experience.
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Strong expertise in Apache Spark and distributed computing, including Spark runtime internals.
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Working knowledge of two or more cloud platforms (AWS, Azure, or GCP), with deep expertise in at least one.
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Experience implementing CI/CD pipelines for production deployments.
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Working knowledge of MLOps practices and deployment workflows.
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Strong understanding of the Databricks Data Intelligence Platform, including its latest features and capabilities.
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Experience with performance tuning, optimization, and scalability of data workloads.
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Strong communication, customer engagement, and consulting skills.
Preferred Skills
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Experience with Delta Lake, Unity Catalog, Lakehouse architecture, and Lakeflow.
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Knowledge of SQL, Python, Scala, and Spark.
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Experience integrating enterprise data platforms and modern analytics architectures.
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Familiarity with DevOps tools such as Git, Azure DevOps, GitHub Actions, Jenkins, or similar.
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Experience supporting enterprise customers in production environments.
Mandatory Requirements
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Databricks Data Engineering Professional Certification.
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Minimum 6–8 completed Databricks implementation projects.
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Strong hands-on development experience on the Databricks platform.
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Expertise in Spark performance tuning and distributed computing.
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Cloud experience across AWS, Azure, or GCP.
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CI/CD and MLOps exposure.
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Current knowledge of Databricks products and platform capabilities.
