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# Data Software Engineer

- **Company:** EPAM Systems, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Continuous Integration, Data Migration, Software Debugging, Standard Sql, Teradata SQL, Macros, Git Flow, Data Programming, Data Analytics, Software Version Control, Data Pipelines, Databricks
- **Published:** August 15, 2026
- **Apply:** https://www.dice.com/job-detail/49a4c27b-c5df-4b44-86c5-db7731d3aecf

## About the Role

requests for data fixes, backfills, or re-runs Collaborate with data engineers and analysts to prevent recurring issues by improving alerting, adding tests, and refactoring fragile models Keep runbooks and support documentation up to date Escalate complex issues appropriately while owning resolution end-to-end Requirements 3+ years of practical experience with dbt, including models, tests, macros, and debugging build/run errors Experience with Databricks, including notebooks, jobs/workflows, clusters, and basic troubleshooting Solid SQL skills with comfort reading and modifying transformation logic Familiarity with git-based workflows, including PRs and version control Strong troubleshooting mindset and ownership of issues through to resolution Good communication skills for working with technical and non-technical stakeholders Proficiency in English at a B2+ level

## Description

We are seeking a Senior Data Software Engineer to support and strengthen a Data & Analytics program focused on scaling the Databricks platform and migrating data and workloads from Teradata. This role requires strong governance around data reconciliation, validation, CI/CD deployment controls, and production release readiness, along with a proactive approach to resolving incidents and improving the reliability of data pipelines. Responsibilities Provide day-to-day support for the Data & Analytics team, triage and resolve incidents raised by stakeholders Diagnose and fix broken or failing dbt models, including compilation errors, test failures, schema drift, and stale dependencies Monitor, debug, and repair Databricks workflows and jobs, including failed runs, cluster issues, and scheduling problems Investigate data quality issues and pipeline failures to identify root cause and apply fixes Maintain and improve dbt tests, documentation, and model configurations Respond to ad hoc


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