Senior Data Engineer

at  ICEYE

REQUIREMENTS

• Strong foundation in data engineering and data modeling concepts
• Experience in building Data Platforms, with Data Mesh principles and Lake Houses
• Hands-on experience in ingestion pipeline development, ETL processes, data modeling, deployment, maintenance, and updates in production environments
• Data-driven mindset and proficiency in various BI Tools like Looker, Tableau, PowerBI or ThoughtSpot
• Experience working with cloud platforms , on-prem Data Warehouses and microservices
• Familiarity with Lakehouse solutions like Databricks (Spark), Snowflake, and orchestration tools like Airflow, dbt, or similar platforms
• Well-versed in design patterns, system architectures, and industry best practices
• Proficient in Python, SQL and Scala(or Java) and experienced in data ingestion from both SQL and NoSQL databases
• Experience with data streaming or near real-time analytics

Non-Technical Skills:

• Effective communication and collaboration skills for working across cross-functional teams
• Demonstrated adaptability and willingness to constantly learn to stay updated on industry trends
• Ability to mentor junior and mid-level analysts on best practices surrounding analytical products

ROLE HIGHLIGHTS:

• Senior Data Engineer
• Finland
• Permanent
• Hybrid

Your responsibilities as Senior Data Engineer include

• Work collaboratively within a cross-domain team to develop new data products and features that drive high-quality analytics and future data initiatives, both internally and externally.
• Enhance and support the data platform for both batch and near-real-time use cases.
• Focus on data delivery, storage, and transformation to improve ICEYE’s data models while ensuring compliance with data regulations.
• Promote best practices in data modeling, with an emphasis on data quality and its application in analytics throughout the organization.
• Adopt a consulting and coaching mindset to foster collaboration and knowledge sharing within and beyond the team.
• Create guidelines and offer coaching on effectively utilizing dashboards and interfaces provided by the data engineering team.
• Enhance and support the data platform for both batch and near-real-time use cases