Senior Data Engineer/Data Team Lead

Senior Data Engineer - 5+ years of experience in software engineering, with meaningful time spent building and operating large-scale data platforms. - Strong hands-on experience with distributed processing engines (Spark strongly preferred), including performance tuning and debugging in production. - Practical experience deploying and operating workloads in Kubernetes-based environments — you're not afraid of infra work; you enjoy it. - Experience building shared frameworks, libraries, or internal tooling used by other engineers, with the product mindset that comes with it (clean APIs, docs, versioning, backward compatibility). Strong proficiency in SQL and data modeling: complex analytical queries, query tuning, partitioning strategies. - Solid software engineering fundamentals in Python (and/or Scala/Java): testing, code review culture, CI/CD. - Experience with cloud-native data infrastructure on AWS (or equivalent) at high scale. - A strong sense of ownership — from design through deployment, operation, and cost. Ability to thrive in a fast-changing environment, making pragmatic decisions with incomplete information. Data Team Lead Management: 3+ years of experience leading a development team or serving as a Tech Lead for data/backend teams. Python Mastery: 4+ years of hands-on experience with Python (Must). Data Engineering: 5+ years of proven experience as a Big Data Backend or Data Engineer.Environments & Infrastructure: Deep understanding of Cloud environments alongside containerized On-Prem infrastructure (OpenShift, Kubernetes, Docker, Linux). Databases: Strong experience with SQL and NoSQL (PostgreSQL and OpenSearch/ElasticSearch – a significant advantage). Data Ops & CI/CD: Proven experience processing large-scale data sets and working with CI/CD methodologies (e.g., GitLab). Education: BSc/BA in Computer Science, Software Engineering, or a related field.