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Metro location and popular data-engineer title balanced by seniority and niche Databricks/LLM requirements.
Deep Databricks, Spark, medallion, and LLM pipeline expertise creates strong domain specificity and limited transferability.
Explicit 10+ years plus mandatory Databricks, Spark, cloud, and platform leadership create stringent filters.
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Lead design and development of scalable, high-performance data pipelines and platforms using Databricks Lakehouse and medallion architecture (bronze/silver/gold).
Architect and manage ETL/ELT workflows, data models, and storage strategies to support analytics, ML, and GenAI workloads including LLM data needs.
Lead technical leadership activities including mentoring engineers, enforcing best practices, managing migrations to cloud-native lakehouse architectures, and collaborating across teams.
10+ years of hands-on data engineering experience including leadership of data platform initiatives.
Strong expertise with Databricks Lakehouse Platform (Delta Lake, Delta Live Tables, Databricks Workflows, Unity Catalog) and Apache Spark (PySpark, Spark SQL).
Proven experience designing and implementing medallion architecture and building ETL/ELT pipelines for batch and streaming data at scale.
Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP) and data security/governance practices.
Experienced in leading large-scale data platform initiatives combining strong engineering fundamentals with technical leadership, mentoring, and stakeholder management.
Knowledgeable in modern data architectures including data lakes, lakehouses, and cloud migrations, with hands-on proficiency in medallion architecture and distributed processing.
Familiar with Large Language Models (LLMs), GenAI data pipeline requirements, and able to translate complex requirements into scalable, reliable data solutions.