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.