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Remote role and generalist senior data engineer title create moderate applicant competition.
Role requires deep Databricks/Azure expertise and leadership, making cross-industry transfers harder.
Explicit 8–15 years requirement plus Databricks, cloud and leadership needs make filtering strict.
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Lead and mentor a team of Data Engineers, defining standards, conducting code reviews, and driving technical design and architectural decisions.
Design, develop, and optimize scalable batch and real-time data pipelines using Databricks, Spark, and cloud-native services on Azure or AWS.
Collaborate with business stakeholders to gather requirements, manage project delivery including scope, risks, and communicate updates to leadership.
8+ years of experience in Data Engineering and Analytics with 3+ years in a technical leadership role.
Strong hands-on expertise in Databricks, Apache Spark, Delta Lake, and Azure services including Azure Databricks, Azure Data Factory, ADLS, Event Hub, and Key Vault.
Proficiency in Python, PySpark, SQL, and experience with cloud platforms Azure and optionally AWS.
Experience in stakeholder communication, requirement gathering, Agile delivery, and cloud security/compliance management.
Experienced technical leader comfortable mentoring and setting engineering standards in data platforms and analytics environments.
Skilled in cloud-native data engineering on Azure (mandatory) with familiarity in AWS as a plus.
Strong operational focus on building and optimizing secure, scalable data architectures including real-time and batch processing with observability and governance frameworks.