Senior Data Engineer

Design scalable data engineering solutions using PySpark and modern distributed data processing frameworks.
Define data ingestion, transformation, and processing architectures aligned with business and analytical objectives.
Design and optimize Snowflake or Delta Lake on Databricks solutions to support enterprise-scale data platforms.
Lead implementation of high-performance batch and streaming data pipelines.
Design and optimize event-driven data architectures using Apache Kafka or Amazon Kinesis.
Define data streaming standards, integration frameworks, and scalable processing patterns.
Architect workflow orchestration solutions using Apache Airflow or Databricks Workflows.
Establish monitoring, scheduling, and operational controls for reliable pipeline execution.
Drive data quality, validation, reconciliation, and governance practices across data engineering solutions.
Design data engineering solutions following modern Lakehouse architecture principles, data observability practices, and platform engineering standards to improve scalability, reliability, and operational visibility.
Drive development of business-focused data products by improving data quality, discoverability, usability, documentation, and trusted data consumption across analytical platforms.
Promote responsible use of AI-assisted engineering capabilities to improve development productivity, testing, documentation, and engineering quality.
Review data pipeline designs and implementations to ensure adherence to engineering, scalability, and performance standards.
Troubleshoot complex data processing, workflow, and streaming platform issues through detailed root cause analysis.
Mentor team members on PySpark, Snowflake, Delta Lake, Kafka, Kinesis, Airflow, and data engineering best practices.
Collaborate with various teams and stakeholders to support end-to-end data platform delivery.