Data Engineer

A senior data engineering leader capable of driving data modernization, transformation, governance and analytics consumption initiatives while leading teams and delivering scalable cloud-native data platforms.

• Build and manage scalable ETL/ELT pipelines for structured, semi-structured, and unstructured data.
• Develop batch and real-time data processing solutions using modern cloud and big data technologies.
• Design and implement data models, data marts, and analytics-ready datasets for reporting, BI, AI, and ML use cases.
• Drive cloud data engineering initiatives across AWS, Azure, or GCP environments.
• Ensure data quality, governance, lineage, security, and compliance across the data ecosystem.
• Collaborate with Business, Data Architects, Data Scientists, and BI teams to deliver business value through data products.
• Lead modernization of legacy data warehouses and support data lakehouse architectures and platform transformations.
• Establish engineering best practices, CI/CD pipelines, Infrastructure as Code (IaC), and automation standards.
• Mentor and lead Data Engineers while conducting architecture reviews and technical governance.
• Optimize platform performance, scalability, reliability, and cloud costs.

Core Technical Skills

• SQL, Python, PySpark, Spark
• Data Modeling (Star Schema, Snowflake, Data Vault)
• Airflow, DataStage, Informatica, Talend, DBT
• BigQuery, Snowflake, Synapse, Redshift
• Kafka, Pub/Sub, Event Hub

Preferred Skills

• Master Data Management (MDM)
• AI/ML & GenAI Data Platforms