Senior DataOps Engineer

The ideal candidate will have over 4 years of hands-on experience in Data Engineering, with a strong focus on building and optimizing data platforms and cloud platforms. Expertise is required in designing and building distributed systems and data pipelines using tools like Astronomer, Fivetran, DBT, and Segment, with a focus on both structured and unstructured data. Proficiency in Python, Scala, SQL, and Spark is necessary for developing scalable data pipelines and automating data workflows. A solid understanding of cloud infrastructure (AWS/Azure) and data platforms (Databricks, Snowflake, Redshift) is essential, along with hands-on experience in Terraform for automating cloud resource management. The candidate should possess a passion for performance tuning, cost optimization, and efficient resource usage, along with an outcome-oriented and experimental interest in AI-driven development practices. Experience with data governance, security practices, and compliance (e.g., SOC II) is also required. Additionally, the ability to quickly learn new technologies and platforms, demonstrate emotional intelligence with strong collaboration and listening skills, and experience working in a rapid-growth or startup environment are crucial. Excellent communication skills for collaborating with distributed teams across time zones are also a must.