About this role

Manage multiple Databricks workspaces (dev/qa/prod);
Configure cluster policies, governance & compliance;
Create & maintain unity catalog objects: catalogs, schemas, grants, service principals, external storage etc;
Monitor and debug failed / long-running jobs using system tables (job_run_timeline, node_timeline, workflow_run);
Troubleshoot cluster crashes, driver OOM, executor failures, memory leaks;
Investigate Python / Spark errors, dependency conflicts (PyPI, WHL, Maven)
Assist users with cluster/job configuration, notebook errors, unity catalog permissions;
Explain platform limitations and best practices to Data engineers;
Maintain confluence pages with platform rules & troubleshooting guides;

Experience in implementation, integration or technical support;
Experience with Linux, Bash;
Confident SQL skills;
Strong experience of working with AWS Infrastructure (ec2, s3, iam, vpc, secret management, sqs);
Experience with Python sufficient for writing small scripts or applications;
Experience of maintaining Databricks jobs & environments;
Experience with REST API/SDK;
Understanding of file-based databases (DeltaLake, Parquet, Hive);
Understanding of of cluster types & node families;
Spoken English at Intermediate level or higher;

Databricks REST API / Databricks SDK;
Working with schema evolution, time travel, vacuum, compaction, z-order;
Debugging corrupted delta tables (conflicting commits, tombstones, missing checkpoints);
Understanding of acid implementation on top of object storage;
Spark knowledge (jobs, partitions, queries), experience in Kafka or similar technology, familiarity with Terraform / Gitlab CI;
Cost monitoring for platform services and objects;
Experience in enterprise Data platforms;

Nearshore candidates preferred, with availability to work EST or Pacific Time hours
Remote work with no time trackers, flexible working hours
Full-time (8 hours/day)
Attractive USD compensation
Paid vacation, holidays, sick leaves

DBA (Databricks)