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Protocol Intelligence
Data-driven signals on your job's competitivenessSenior metro role with a common Data Engineer title but requires niche Azure Databricks Lakehouse skills.
Deep Lakehouse and Azure Databricks expertise required, transferable technically but domain-heavy for analytics leadership.
Explicit 10–15 years plus mandatory Azure Databricks, PySpark, Delta Lake, data governance, and leadership experience.
Job Description
Structured overview of role & requirementsAbout This Role
Lead the design, development, and implementation of scalable data engineering solutions within a Lakehouse architecture.
Define technical approaches for data ingestion, transformation, integration, and consumption using Azure Databricks, PySpark, SQL, and Delta Lake.
Provide technical leadership and mentorship to Data Engineers and ensure alignment with data architecture, governance, and delivery standards.
Minimum Requirements
10-15 years professional experience in data engineering or related roles.
Strong expertise in Lakehouse architecture, Azure Databricks, Apache Spark, PySpark, Delta Lake, and enterprise-scale ETL/ELT pipelines.
Expert-level proficiency in SQL and hands-on experience with Python/PySpark.
Graduation/Post Graduation required.
Ideal Candidate Profile
Experienced in designing layered data architectures (Bronze, Silver, Gold) and dimensional data models (Star Schemas, Fact/Dimension models).
Capable of translating complex business requirements into scalable technical solutions and engaging with senior technical and business stakeholders.
Proven track record in leading complex data engineering initiatives, driving modernization/migration efforts, and establishing best practices and standards.