Trystar is looking to solve the problem of designing, developing, and maintaining a data platform to support its various business needs, including building and optimizing data pipelines, ensuring data quality and integrity, and implementing data processing systems.
Requirements
- Proficiency in SQL, Python, and ETL tools.
- Experience with cloud data platforms Microsoft fabric, Azure Synapse, Azure Data Factory, Azure Databricks, Power BI or similar.
- Strong understanding of data modeling, data warehousing, data lake houses, semantic models etc.
- Knowledge of data governance principles and best practices, with a commitment to ensuring data quality and integrity.
- Familiarity with digital manufacturing and related systems like ERP, CRM, QMS etc.
- Ability to troubleshoot technical issues and implement minor system enhancements.
- Experience with managing large-scale data projects, including planning, execution, and delivery.
Responsibilities
- Act as the strategic point of contact for all data engineering initiatives, collaborating with cross-functional teams to design, build, and maintain robust data pipelines.
- Develop and implement scalable data solutions to support analytics, machine learning, and business intelligence activities.
- Optimize and manage the performance of databases, ensuring data integrity and high availability.
- Collaborate with stakeholders to gather, document, and translate business requirements into efficient data architecture.
- Provide hands-on support and mentoring for junior data engineers, empowering them to effectively contribute to Trystar’s data solutions.
- Orchestrate data validation and testing procedures to ensure data quality and alignment with operational goals.
- Develop and maintain detailed documentation for data processes, customizations, and resolutions.
Other
- Bachelor’s degree in computer science, Data Engineering & Analytics or a related field.
- Minimum of 7 years of experience in data engineering, with a focus on designing and implementing data solutions at scale.
- Proven ability to lead and mentor a team of data engineers, ensuring high performance and continuous improvement.
- Excellent analytical and problem-solving skills, with a strategic approach to data management and architecture.
- Willingness and ability to travel 20%
