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.

• Demonstrates strong ownership while driving data engineering excellence.

• Collaborate effectively with various teams and business stakeholders to ensure smooth delivery.

• Promotes quality-focused engineering through proactive validation, optimization, and continuous improvement.

• Apply strong analytical thinking to evaluate complex data engineering and platform challenges.

• Demonstrate adaptability while managing evolving technologies, data ecosystems, and business requirements.

• Communicates effectively regarding delivery status, risks, dependencies, and improvement opportunities.

• Maintains high attention to detail across data architecture, pipeline design, testing, and implementation activities.

• Encourages continuous improvement in data engineering practices and platform operations.

• Supports knowledge sharing and mentoring to strengthen team capabilities.

• Balances scalability, performance, reliability, and business priorities while driving delivery excellence.

• Promotes innovation by adopting modern data engineering practices, platform engineering principles, and AI-assisted development approaches to improve engineering productivity and solution quality.