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Protocol Intelligence
Data-driven signals on your job's competitivenessBroad senior data-engineer skillset, metro location, and generalist migration project increase candidate competition.
Strong data-engineering fundamentals are transferable, but migration and legacy-platform expertise raise industry-specific fit sensitivity.
Explicit 8+ years, specific tech stack and migration experience make shortlisting stringent.
Job Description
Structured overview of role & requirementsAbout This Role
Lead end-to-end datastore migration from an on-prem DataLake Hadoop ecosystem to an AWS-hosted LakeHouse.
Manage migration tooling issues including investigation, root cause analysis, and coordination with tooling teams for resolution.
Translate and optimize legacy SQL and Spark patterns for Snowflake and Iceberg compatibility ensuring functional data equivalence with reconciliation frameworks.
Minimum Requirements
6 to 9 years total experience with at least 8 years in Data Engineering.
3 to 5 years of professional hands-on coding experience in Python/Java and SQL in team environments.
Bachelor's or Master's degree in Computer Science, Applied Mathematics, Engineering, or related quantitative field.
Proficiency in Kafka, Apache Spark, Snowflake, Hadoop ecosystem (HDFS/Hive), AWS S3, CI/CD pipelines, and experience with RESTful APIs.
Ideal Candidate Profile
Experience migrating large-scale legacy Data Lakes to cloud LakeHouse architectures, specifically AWS-hosted systems.
Strong expertise in temporal data modeling (e.g., unitemporal, bitemporal), data reconciliation, and schema evolution management strategies.
Comfortable working in high-visibility projects requiring detailed technical root cause analysis and collaborative issue resolution across teams.