Data Engineer

Requirements:

• 11+ years of experience in data engineering, with at least 2 years in a managerial role.
• Experience in any SaaS company is highly beneficial, as is an understanding of common SaaS business processes, including familiarity with metrics such as customer acquisition costs, retention rates, and revenue growth.
• Technical Expertise: Strong knowledge of data engineering concepts, including data warehousing, ETL processes, and data pipeline design. Proficiency in SQL, Python, and other data engineering tools.
• Data Modeling: Expertise in data modeling is essential, with the ability to design and implement robust, scalable data models that support complex analytics and reporting needs. Experience with data modeling frameworks and tools is highly valued.
• Leadership Skills: Proven ability to lead and motivate a team of engineers while managing cross-functional collaborations.
• Problem-Solving: Strong analytical and troubleshooting skills to address complex data-related challenges.
• Communication: Excellent verbal and written communication skills to effectively interact with technical and non-technical stakeholders. This includes the ability to motivate team members, provide regular constructive feedback, and facilitate open communication channels to ensure team alignment and success.
• Data Architecture: Experience with designing scalable, high-performance data systems and understanding cloud platforms such as AWS, Google Cloud, Data Bricks, Clickhouse, Snowflake, or Azure.
• Machine Learning and GenAI: Knowledge of machine learning concepts and integration with data pipelines, as well as familiarity with GenAI, is a plus.
• Data Governance: Experience with data governance best practices is desirable.
• Open Mindset: An open mindset with a willingness to learn new technologies, processes, and methodologies is essential. The ability to adapt quickly to evolving data engineering landscapes and embrace innovative solutions is highly valued.
• Skills Required: Data Modeling, Data Architecture, SQL, Data Engineering, AWS, Python.