At CRG, we are seeking an experienced Lead BI Engineer to lead the design, development, and optimization of scalable Business Intelligence solutions. This role is responsible for driving data strategy, building robust data models and reporting platforms, and mentoring BI engineers while partnering with cross-functional stakeholders to deliver actionable insights that support business decision-making.

Requirements:

· 7+ years of experience in advanced data engineering with distributed compute technologies.

· Expert‑level Spark engineering (performance tuning, cluster configuration, partition strategies, optimization of large datasets).

· Hands‑on experience with Lakehouse architectures including ACID transactions, schema evolution, and governance frameworks.

· Deep proficiency in Python and SQL for large‑scale data transformation.

· Experience supporting machine‑learning pipelines or model operationalization.

· Proven experience architecting cloud‑native data platforms (Azure, AWS, or GCP).

· Strong background integrating diverse, complex data sources at enterprise scale.

· Demonstrated ability to own mission‑critical production systems.

· Experience with distributed streaming frameworks (Kafka, Event Hubs, or similar).

· Experience building or supporting ML platforms, feature stores, or experiment‑tracking systems.

· Background in data security, compliance controls, or audit‑ready governance.

· Experience automating data operations with CI/CD and infrastructure‑as‑code.

Responsibilities:

· Architect, build, and optimize distributed data pipelines using Apache Spark in a high‑volume, mission‑critical environment.

· Design and maintain enterprise Lakehouse architecture with Delta Lake, ensuring ACID compliance, lineage, auditability, and data governance.

· Develop automated ingestion frameworks (batch, streaming, and event‑driven) across multiple cloud services and integration points.

· Enable machine‑learning workflows by preparing feature‑ready datasets and establishing reproducible ML deployment patterns.

· Lead platform‑wide data quality, access control, and cataloging frameworks.

· Implement advanced cost‑optimization, cluster tuning, and performance engineering strategies.

· Collaborate with Finance, BI, Operations, and ML teams to translate complex business needs into scalable data solutions.

· Own production reliability, troubleshooting, and root‑cause analysis for data and ML pipelines.