AWS Data Engineer

. Define and drive enterprise data engineering strategy aligned with organizational objectives and data modernization initiatives.

. Establish data engineering standards, governance frameworks, and best practices across teams.

. Lead the design of enterprise-scale data processing architectures using PySpark and modern data platform technologies.

. Define enterprise standards for Snowflake and Delta Lake-based data platforms supporting analytical and operational workloads.

. Drive real-time and event-driven data architecture initiatives using Apache Kafka or Amazon Kinesis.

. Establish governance standards for data ingestion, transformation, streaming, and processing frameworks.

. Define workflow orchestration, scheduling, and operational governance standards using Apache Airflow or Databricks Workflows.

. Establish data quality, validation, monitoring, and operational excellence frameworks across data engineering ecosystems.

. Define enterprise standards for data products, data quality ownership, metadata management, discoverability, and trusted business data consumption across the organization.

. Establish architecture standards for modern Lakehouse platforms, data observability, platform engineering, and scalable cloud-native data ecosystems supporting enterprise analytics and AI initiatives.

. Define AI-ready data foundation strategies supporting structured and unstructured data processing, vector-enabled architectures, retrieval patterns, and future GenAI and Agentic AI initiatives.

. Partner with business stakeholders to translate business objectives into scalable data platform capabilities, data products, and enterprise data architecture decisions.

. Drive adoption of AI-assisted engineering practices across data engineering teams to improve developer productivity, code quality, documentation, testing, and delivery effectiveness while maintaining governance standards.

. Lead architecture reviews and ensure data solutions meet scalability, reliability, maintainability, and performance objectives.

. Guide teams on distributed data processing, streaming architectures, modern data platforms, and engineering best practices.

. Identify platform risks, scalability bottlenecks, operational gaps, and architectural challenges while defining mitigation strategies.

. Collaborate with various teams and leadership stakeholders to align data initiatives with organizational objectives.

. Drive continuous improvement initiatives focused on platform maturity, engineering excellence, scalability, reliability, and delivery effectiveness.

. Demonstrates leadership and accountability in driving data engineering excellence across programs and initiatives.

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

. Promotes a culture of innovation, quality, continuous improvement, and data engineering excellence.

. Apply strategic thinking to address platform challenges, scalability risks, and business priorities.

. Demonstrates strong decision-making while balancing performance, scalability, reliability, and delivery objectives.

. Communicates effectively regarding platform strategy, risks, dependencies, and operational outcomes.

. Maintains a proactive approach toward engineering governance, platform standards, and solution excellence.

. Encourages innovation and continuous learning within data engineering, streaming platforms, and modern data architecture.