AWS Data Engineer Job Description

Experience: 8-10 Years

Location: Noida

Mandatory Skills

- Python
- PySpark
- SQL
- Apache Kafka
- Snowflake
- Databricks Workflows

Key Responsibilities
- 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, contemporary 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.

Behavioral Competencies
- 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.

📌 AWS Data Engineer (Noida)
🏢 IRIS SOFTWARE
📍 Noida