- Framework Development: Design and build metadata-driven, generic data ingestion frameworks (batch, streaming, and CDC) to automate the onboarding of current data sources.
- Architecture & Design: Define architectural standards for the Medallion architecture (Bronze, Silver, Gold layers) utilizing Delta Lake.
- Technical Leadership: Lead technical design discussions, mentor junior/mid-level engineers, and conduct rigorous code reviews.
- Pipeline Optimization: Optimize Spark jobs for performance, scalability, and cost reduction across the Databricks platform.
- CI/CD & DevOps: Implement and mature CI/CD pipelines (Git, automated testing) for data platforms and enforce DataOps best practices.
- Governance & Security: Integrate ingestion pipelines with Unity Catalog for enterprise-grade data governance and lineage tracking.
Required Qualifications
- Experience: 8+ years in Data Engineering,
with at least 3–4 years of hands-on technical leadership experience leading engineering pods.
- Core Databricks & Spark: Advanced proficiency in PySpark and Databricks SQL.
- Ingestion Expertise: Proven experience building custom or highly configurable data ingestion frameworks for structured and unstructured data.
- Cloud Platforms: Strong experience working with AWS, Azure, or GCP cloud-native services (e.g., S3, ADLS).
- Engineering Standards: Deep understanding of data modeling (star/snowflake schemas), SCD (Slowly Changing Dimensions), and software engineering principles in a data context.
- Soft Skills: Excellent stakeholder management and the ability to translate complex technical architectures into business value.
Preferred Qualifications
- Experience with Kafka, dbt, or Delta Live Tables (DLT).
- Databricks Certified Data Engineer Associate/Professional credentials
Interested? Please apply, thanks for your interest and time.
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📌 Databricks Tech Lead (Sydney)
🏢 FR Consultancy
📍 Sydney
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