{"id":3061125,"url":"https://alion.io/job/hawk-sense-business-solution-forward-deployed-engineer-data-engineering","title":"Forward Deployed Engineer - Data Engineering","company":{"id":3800682,"name":"Hawk Sense Business Solution","domain":"hawk-sense.com","url":"https://alion.io/company/hawk-sense-business-solution-pvt-ltd","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Solutions","role_family":"Solutions","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":19500,"max_usd":43000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":13},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Apache Kafka","optional":false},{"name":"Azure","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"GitHub Actions","optional":false},{"name":"LLM","optional":false},{"name":"pySpark","optional":false},{"name":"RAG","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"AWS","optional":true},{"name":"GCP","optional":true},{"name":"Hallucination","optional":true},{"name":"Python","optional":true}],"status":"live","first_seen_at":"2026-10-22T06:07:09Z","employer_posted_date":null,"last_verified_at":"2026-10-22T06:07:09Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"Databricks Forward Deployed Engineer :\n\nRole Overview :\n\nThe 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.\n\nKey Responsibilities :\n\nClient Engagement & Solution Design :\n\n- Lead technical discovery sessions to understand client data platform challenges and objectives\n\n- Design end-to-end Lakehouse architectures (Medallion, streaming, batch, and hybrid patterns)\n\n- Present architecture recommendations and trade-off analyses to technical and executive stakeholders\n\n- Produce solution architecture documents, data flow diagrams, and design decision records\n\nPlatform Engineering :\n\n- Build production data pipelines using PySpark, Spark SQL, and Delta Lake on Databricks\n\n- Implement ingestion patterns using AutoLoader, Kafka, and event-driven architectures\n\n- Implement SCD Type 1/2/3 and CDC patterns for dimension and fact table management\n\n- Optimise Delta Lake tables (OPTIMIZE, VACUUM, Liquid Clustering, Z-Ordering, Deletion Vectors)\n\n- Right-size and configure Databricks clusters\n\n- Configure Unity Catalog (data lineage, access controls, workspace federation)\n\nPerformance & Production Engineering :\n\n- Diagnose performance bottlenecks using Spark UI\n\n- Implement CI/CD pipelines using Databricks Asset Bundles (DABs), GitHub Actions, or Azure DevOps\n\n- Define data quality checks, SLA monitoring, and production alerting frameworks\n\nGenAI & Advanced Analytics :\n\n- Support GenAI solution design (RAG pipelines, vector store integration, LLM evaluation)\n\n- Leverage Mosaic AI and Databricks Genie for ML and analytics workloads\n\nRequired Qualifications :\n\n- 5+ years of data engineering experience in production environments\n\n- 2+ years of hands-on Databricks or Apache Spark platform delivery at scale\n\n- Prior client-facing, consulting, or embedded engineering experience\n\nTechnical Skills :\n\n- Deep PySpark and Spark SQL proficiency\n\n- Hands-on Delta Lake expertise\n\n- Strong Databricks cluster architecture knowledge\n\n- Strong SQL (window functions, CTEs, analytical queries)\n\n- CI/CD hands-on (DABs, notebooks-as-code)\n\n- Unity Catalog or equivalent data governance frameworks\n\nCommunication & Soft Skills :\n\n- Ability to present complex technical decisions clearly to non-technical stakeholders\n\n- Structured problem-solving and ownership mindset\n\nPreferred Qualifications :\n\n- Databricks Certified Data Engineer Associate or Professional certification\n\n- Experience with Structured Streaming, Delta Live Tables, or Kafka\n\n- GenAI / LLM exposure (RAG, vector databases, hallucination mitigation)\n\n- Cloud platform experience (Azure preferred, AWS, or GCP)\n\n- Prior experience within a DPP, GSI, or consulting delivery model\n\nSkills\nPython, Azure, Generative AI, Databricks, PySpark, Delta Lake, SQL, Kafka, Forward Deployment 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