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Medium competition: common senior data-engineer role with Databricks, Spark, cloud requirements.
Medium — core data engineering skills are transferable, but Databricks/client-facing consulting adds specificity.
High — explicit 6+ years plus expert Spark/Databricks, cloud depth, and DataOps mandates.
Lead technical delivery on complex client data engineering projects, collaborating directly with client data leadership to solve challenging issues.
Design, build, and productionize scalable, reliable data pipelines and lakehouse architectures primarily using Databricks and cloud platforms.
Set engineering standards through hands-on coding, mentorship, and championing DataOps practices across projects.
6+ years of hands-on data engineering experience delivering production-grade data platforms.
Expert level proficiency in Apache Spark including runtime internals and performance tuning.
Hands-on experience with Databricks (Delta Lake, Unity Catalog, Workflows); Databricks certification is a strong plus.
Experience working with at least two major cloud platforms (AWS, Azure, or GCP) with depth in one, including cloud-native data services.
Strong communicator capable of engaging technical and non-technical stakeholders including client leadership and junior engineers.
Demonstrated ability to architect for scale and reliability in production-grade data platforms using modern data stacks.
Experienced in implementing DataOps principles: CI/CD pipelines, automated testing, observability, and infrastructure-as-code.