Senior Data Engineer (Graph Databases & Databricks)
New
D
DaCodesSoftware Development
Candidates located in LATAM or Mexico. Mexico. Chile. Colombia. Uruguay. Guatemala. Paraguay, Availability to collaborate with teams across the Americas time zones.ContractSenior
Salary not disclosed
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Job Details
- Languages
- Advanced English (C1+ preferred).
- Experience
- 6+ years of experience in Data Engineering, Database Engineering, or Data Platform development. 3+ years working with enterprise-scale data platforms.
- Required Skills
- PostgreSQLSQLETLGitDatabricks
Requirements
- 6+ years of experience in Data Engineering, Database Engineering, or Data Platform development.
- 3+ years working with enterprise-scale data platforms.
- Strong Databricks experience.
- Advanced SQL and PostgreSQL expertise.
- Deep knowledge of recursive CTEs, query optimization, query plan analysis, indexing strategies, and materialized views.
- Hands-on experience with at least two graph database technologies (e.g., Neo4j, TigerGraph, Nebula Graph, ArangoDB, Memgraph).
- Strong Cypher knowledge.
- Experience building APIs for data access and performance testing.
- ETL/ELT development experience.
- Git and version control best practices.
- Advanced English (C1+ preferred).
- Bachelor's degree in Computer Science, Software Engineering, Information Systems, Data Science, or related field.
Responsibilities
- Design and execute large-scale graph database evaluations and benchmarking initiatives.
- Extract, transform, and load data from Databricks into multiple graph database platforms.
- Build and maintain graph structures, entity relationships, and linkages that replicate production-grade data models.
- Implement benchmark workloads using graph query languages such as Cypher, GSQL, openCypher, nGQL, or AQL.
- Develop PostgreSQL baseline implementations using recursive CTEs and advanced querying techniques.
- Design and build APIs to evaluate real-world query performance and scalability.
- Analyze execution plans, indexing strategies, and query optimization opportunities.
- Continuously provision, configure, and tear down database environments for controlled testing.
- Document findings, benchmark results, technical trade-offs, and recommendations.
- Collaborate with architects, data engineers, and technical leadership teams throughout the evaluation process.
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