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

∙Solid 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

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