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Lead Data Engineer

PublishedPublished: 6/14/2022
Technology

Job Description

Programming & Data Engineering

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  • Expert - level proficiency in Python, Scala, and PySpark, with a strong track record of designing and delivering production-ready, modular, and well-tested solutions; developing and troubleshooting Spark workloads; and optimizing large-scale batch and streaming data pipelines using Delta Lake and Spark technologies.
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  • Strong SQL and data modelling dimensional and normalised; schema design and data contract definition.
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  • Databricks expertise Delta Lake, Unity Catalog, Jobs & Workflows, cluster and pool management, performance tuning, Model Serving.
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  • Azure data stack — ADLS Gen2 (zone design, ACLs, lifecycle), Azure Data Factory (parameterized / metadata-driven frameworks, error handling), Azure Event Hubs.
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AI & Machine Learning

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  • 3+ years designing and shipping LLM-based systems in production: RAG pipelines, agentic / tool-calling workflows, structured output, chunking and embedding strategy, vector and hybrid retrieval, and prompt engineering.
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  • Evaluation discipline — golden datasets, regression suites, accuracy and hallucination tracking, human-in-the-loop feedback loops; you measure AI quality, not assert it.
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  • Hands-on experience with LangChain, LlamaIndex, or LangGraph, plus at least one provider stack (Azure OpenAI, OpenAI, or Databricks Model Serving).
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  • Metadata-driven thinking — schema inference, data profiling, lineage, catalogs, and configuration-driven frameworks that onboard the next source without new code.
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Architecture & Governance

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  • 12–18 years of total experience in data engineering, data platform delivery, or related disciplines.
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  • Proven delivery of a medallion/lakehouse architecture at enterprise scale — not just familiarity with the concept.
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  • Azure security and governance: Entra ID, managed identities, RBAC, POSIX ACLs on ADLS Gen2, Key Vault, private endpoints, and PII handling.
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  • CI/CD and infrastructure as code: Azure DevOps, Terraform, Databricks Asset Bundles, and automated testing of data pipelines.
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  • Clear technical writing and the ability to present and defend a design to both engineers and non-technical stakeholders.
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