Sr Advanced Data Engineer

The Sr Advanced Data Engineer – AI‑Ready Data Platforms is responsible for architecting, building, and optimizing large‑scale data systems that power Honeywell Aerospace’s enterprise data strategy and AI‑ready data layer .

This role plays a critical part in ensuring that the organization’s data platforms are scalable, governed, performant, and aligned to AI and advanced analytics use cases . The Sr Advanced Data Engineer partners closely with AI/ML teams, data scientists, platform teams, and business stakeholders to ensure that data is available, trusted, and production‑ready to support analytics, advanced analytics, and AI initiatives in a timely manner.

Key Responsibilities

Architecture & System Design

• Design and own end‑to‑end, scalable enterprise data architectures , including:
  • Data Lake
  • Data Mesh
  • Medallion (Bronze / Silver / Gold) architectures
• Align data architecture decisions with long‑term business goals and AI strategy
• Select, evaluate, and standardize the enterprise data technology stack , including:
  • Cloud‑native data services
  • Snowflake enterprise data warehouse
  • Databricks analytical data lake platforms
• Actively participate in AI initiatives , ensuring the data layer is AI‑ready and fit for enterprise AI consumption
  

Pipeline & Infrastructure Development

• Build, manage, and optimize complex ETL / ELT pipelines using tools such as:
  • Apache Airflow
  • Azure Data Factory
  • AWS Glue
  • Informatica
• Design and implement real‑time and near‑real‑time data pipelines using:
  • Apache Kafka
  • Spark Structured Streaming
• Establish standardized data ingestion and transformation pipelines across enterprise systems
• Ensure high‑quality, timely availability of data for analytics, advanced analytics, and AI use cases
  

Performance Tuning & Optimization

• Identify and resolve performance bottlenecks in distributed data systems
• Optimize query performance, processing latency, and cloud costs through:
  • Partitioning strategies
  • Clustering
  • Indexing
• Work closely with data platform and cloud teams to ensure adoption of latest data technologies and optimizations
  

Data Governance, Quality & Observability

• Define and enforce enterprise data quality standards using frameworks such as Great Expectations
• Implement and support data governance, lineage, and observability tools
• Ensure compliance with global data regulations (e.g., GDPR, CCPA) by implementing:
  • Data encryption
  • Role‑Based Access Control (RBAC)
• Maintain strong guardrails for data usage, access, and quality across the enterprise
  

Leadership, Collaboration & Mentorship

• Provide technical leadership and guidance to junior and mid‑level data engineers
• Conduct code reviews and promote best practices in documentation and data engineering standards
• Act as a technical bridge between leadership, data scientists, AI teams, and business stakeholders
• Translate business and AI requirements into actionable, scalable data solutions

YOU MUST HAVE

Experience & Capabilities

• 8–12 years of experience in data engineering or advanced data platform roles
• Proven experience designing and operating enterprise‑scale data platforms
• Strong hands‑on experience building AI‑ready, governed, and automated data layers
• Experience working in large, global, and regulated enterprise environments
  

Advanced Skill Requirements

Core Languages

• Expert proficiency in Python and SQL

Big Data & Analytics Platforms

• Deep experience with:
  • Snowflake (enterprise data warehouse)
  • Databricks (analytical data lake platforms)
• Strong understanding of distributed data processing concepts

Cloud Platforms

• Hands‑on experience with AWS, Azure, and/or Google Cloud Platform (GCP) , including services such as:
  • S3 / ADLS
  • BigQuery
  • Redshift

Emerging & Advanced Technologies

• Familiarity with Vector Databases to support AI and LLM use cases
• Experience implementing CI/CD pipelines for data engineering workloads
  

Education

• Bachelor’s or Master’s degree in Engineering , Computer Science, Information Technology, Data Engineering, or a related field
  

Who Will Succeed in This Role

• Experienced data engineers who can design, build, and scale enterprise data platforms
• Professionals who ensure the data layer is robust, governed, automated, and AI‑ready
• Engineers with strong focus on performance, accuracy, reliability, and compliance
• Individuals who can support analytics, advanced analytics, and AI applications with high‑quality, trusted data
  

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