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Strong employer brand, mid-level data engineer title, and metro location drive high applicant competition.
Core data engineering skills (Python, SQL, ETL, cloud) are highly transferable across industries, so sensitivity is low.
Explicit 5+ years plus many mandatory data engineering technologies and tools increases filtering strictness.
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Design, build, and maintain scalable, reliable data pipelines and architecture using Python, SQL, distributed data frameworks, and cloud platforms (AWS, Azure, or GCP).
Integrate and manage large, complex datasets meeting functional and non-functional requirements, ensuring high data quality and consistency.
Lead project and stakeholder management for delivering data products and infrastructure supporting analytics, data science, and cross-functional teams.
Minimum 5 years of experience in data engineering roles with hands-on expertise in Python, distributed data processing frameworks (e.g., Databricks), cloud data platforms, and AWS or equivalent clouds.
Experience with workflow orchestration/scheduling platforms, version control systems (GitHub, GitLab, Bitbucket, Azure Repos), and CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions, Azure DevOps).
Strong knowledge of API design and RESTful services, system design for scalable and fault-tolerant data architectures, and big data/distributed technologies (streaming, messaging, search/indexing, container orchestration).
Work Experience Required: Minimum 5 years in data engineering role. Notice period: Not explicitly mentioned in the JD.
Experienced in designing and operating data pipelines in cloud and distributed systems with proven end-to-end implementation expertise.
Strong technical collaborator capable of engaging with executive, product, data, and design stakeholders to manage delivery and address data infrastructure challenges.
Comfortable leading data engineering projects with accountability for timely, high-quality delivery of data products and infrastructure in Agile environments.