Bachelors degree in Computer Science or a related quantitative field; relevant professional experience may be considered in lieu of formal education.
46 years of experience working as a Data Engineer, with demonstrated experience spearheading complex data engineering initiatives.
Strong expertise in Python and SQL for large-scale data processing, transformation, and platform development.
Advanced hands-on experience with Apache Spark (PySpark) for distributed data processing and optimization.
Strong experience with Kafka and event-driven architectures for real-time data ingestion and stream processing.
Extensive experience designing, developing, and maintaining production-grade Airflow workflows.
Deep understanding of Amazon Redshift architecture, performance tuning, schema design, workload management, and query optimization.
Strong knowledge of data modeling techniques including dimensional modeling, data vault, and scalable warehouse design fundamentals.
Experience designing cloud-native data platforms and data lake architectures on AWS.
Hands-on experience implementing data quality, observability, lineage, governance, and monitoring frameworks.
Strong understanding of software engineering best practices including CI/CD, Infrastructure as Code, version control, testing, and deployment automation.
Experience with architecture design, solution reviews, technical documentation, and system scalability planning.
Excellent stakeholder management, analytical, problem-solving, and communication skills.
Demonstrated experience mentoring engineers and providing technical leadership within a team environment.
Adaptable and able to thrive in a fast-paced, dynamic environment.

Lead Data Engineer

Job Description

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