Data Engineer designing data pipelines in Python for a major railway industry client. Collaborate with Data Scientists and ensure code quality with agile methodologies.
About the role
- Data Engineer II at Honeywell responsible for data architecture, pipelines, and analytics support. Collaborating across teams to ensure data quality and compliance in high-volume environments.
Responsibilities
- Design, implement, and manage the data architecture, systems, and processes to effectively collect, store, process and analyze high volume, high dimensional data
- Create and maintain scalable, efficient, and secure data pipelines, data warehouses, and data lakes
- Ensure consistency in data quality and availability for analysis and reporting including compliance with data governance and security standards
- Work in complex data science and analytics projects in support of the Commercial organization.
- Collaborate with data scientists and analysts to define and structure data for effective analysis and reporting.
- Develop and maintain ETL (Extract, Transform, Load) processes
- Administer, optimize, and manage databases, data warehouses, and data lakes to ensure performance, reliability, and scalability.
- Enforce data governance policies, standards, and best practices to maintain data quality, privacy, and security.
- Create and maintain comprehensive documentation for data architecture, processes, and systems.
Requirements
- 4 + years of relevant experience in Data Engineering, ETL Development, Database Administration.
- Experience in Azure Databricks, CI/CD & Dev Ops Process
- Expert in scripting and querying languages, such as Python, SQL, PySpark
- Experience with both Structured and Unstructured data
- Experience in Snowflake
- SFDC business/ technical knowledge
- Knowledge of Agile development methodology
- Working with at least one NoSQL system (HBase, Cassandra, MongoDB)
- Knowledge of databases, data warehouse platforms (Snowflake) and Cloud based tools.
- Experience in using data integration tools for ETL processes.
- Knowledge of Data Modelling techniques including schema design for both rational and NoSQL databases
- Understanding of Hadoop's ecosystem (including HDFS) and Spark for processing and analyzing large-scale datasets.
- Demonstrated experience in cutting-edge packages such as SciKit, TensorFlow, Pytorch, GPT, PySpark, Bit bucket etc.
- Ability to develop and communicate technical vision for projects and initiatives that can be understood by customers and management.
- Proven mentoring ability to drive results and technical growth in peers.
- Effective communication skills (verbal, written, and presentation) for interacting with customers and peers.
- Demonstrated application of statistics, statistical modeling, and statistical process control.
Benefits
- Access to dynamic career opportunities across different fields and industries
- Amazing opportunities for career growth
- Access to a world-class team of diverse experts
Job title
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Not specified
Degree requirement
No Education Requirement
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