Forward Deployed Engineer
Databricks Forward Deployed Engineer (FDE)
Function
Data & AI Engineering
No of Roles- 20 roles
Senior G7- 5-8 yrs
Location- PAN India Location
Upto : 22 LPA
Mandatory skills- Python, react, azure, gen AI, CICD
Role Overview
The Databricks Forward Deployed Engineer (FDE) is a senior client-facing engineer embedded
with enterprise clients to design, build, and optimise production-grade data platforms on
Databricks. The role requires deep Spark and Databricks technical expertise combined with
strong client communication skills — the FDE leads technical engagements end-to-end, makes
defensible architecture decisions, and delivers measurable platform outcomes.
Key Responsibilities
Client Engagement & Solution Design
∙Lead technical discovery sessions to understand client data platform challenges and
objectives
∙Design end-to-end Lakehouse architectures — Medallion (Bronze/Silver/Gold),
streaming, batch, and hybrid patterns
∙Present architecture recommendations and trade-off analyses to technical and executive
stakeholders
∙Produce solution architecture documents, data flow diagrams, and design decision
records
Platform Engineering
∙Build production data pipelines using PySpark, Spark SQL, and Delta Lake on Databricks
∙Implement ingestion patterns using AutoLoader, Kafka, and event-driven architectures
∙Implement SCD Type 1/2/3 and CDC patterns for dimension and fact table management
∙Optimise Delta Lake tables — OPTIMIZE, VACUUM, Liquid Clustering, Z-Ordering, and
Deletion Vectors
∙Right-size and configure Databricks clusters — Job vs All-Purpose, Driver/Worker ratios,
auto-scaling, spot instances
∙Configure Unity Catalog — three-level namespace, data lineage, access controls, and
workspace federation
Performance & Production Engineering
∙Diagnose performance bottlenecks using Spark UI — stage analysis, shuffle, data skew,
and OOM resolution
∙Implement CI/CD pipelines using Databricks Asset Bundles (DABs), GitHub Actions, or
Azure DevOps
∙Define data quality checks, SLA monitoring, and production alerting frameworks
∙Quantify performance improvements and cost savings in business terms
GenAI & Advanced Analytics (Basic)
∙Support GenAI solution design — RAG pipelines, vector store integration, and LLM
evaluation on Databricks
∙Leverage Mosaic AI and Databricks Genie for ML and analytics workloads
Required Qualifications
Experience
∙5+ years of data engineering experience in production environments
∙2+ years of hands-on Databricks or Apache Spark platform delivery at scale
∙Prior client-facing, consulting, or embedded engineering experience
Technical Skills
∙Deep PySpark and Spark SQL proficiency — execution model, stage/shuffle/task lifecycle,
performance tuning
∙Hands-on Delta Lake expertise — transaction log, ACID, time travel, schema evolution,
compaction
∙Strong Databricks cluster architecture knowledge — AQE, dynamic allocation, pool
configuration
∙Experience designing and delivering Medallion Architecture for enterprise Lakehouse
platforms
∙AutoLoader proficiency — incremental ingestion, schema inference and evolution,
duplicate handling
∙Strong SQL — window functions, CTEs, analytical queries, query optimisation
∙CI/CD hands-on — DABs, notebooks-as-code, multi-environment deployment pipelines
∙Unity Catalog or equivalent data governance and access control frameworks
Communication & Soft Skills
∙Ability to present complex technical decisions clearly to non-technical stakeholders
∙Structured problem-solving — can diagnose unfamiliar issues and arrive at solutions
under ambiguity
∙Ownership mindset — proactively identifies delivery risks and escalates effectively
Preferred Qualifications
∙Databricks Certified Data Engineer Associate or Professional certification
∙Experience with Structured Streaming, Delta Live Tables, or Kafka streaming
architectures
∙GenAI / LLM exposure — RAG, vector databases, embedding pipelines, hallucination
mitigation
∙Cloud platform experience — Azure (preferred), AWS, or GCP
∙Prior experience within a DPP, GSI, or consulting delivery model