Database Engineer
Responsibilities:
• Oversee daily health and uptime of Databricks pipelines, job schedules, compute resources, Delta Lake datasets, and warehouse environments to maintain consistent platform performance.
• Design, build, and refine Spark-based data processing workflows in Databricks notebooks across bronze, silver, and gold layers, including incremental ingestion patterns using Auto Loader and streaming frameworks when needed.
• Support and enhance workflow orchestration through Databricks Workflows and Apache Airflow, with attention to data delivery timelines, reliability targets, and operational continuity.
• Administer data governance controls in Unity Catalog by managing schemas, permissions, lineage visibility, and catalog structures within a regulated environment.
• Maintain and optimize Microsoft SQL Server databases used as source and downstream data stores, improving query execution, indexing strategy, schema organization, and overall system efficiency.
• Track compute utilization and platform spend by implementing monitoring, alerting, and resource tuning practices that improve predictability and cost control.
• Investigate and resolve end-to-end production issues involving failed jobs, data validation concerns, evolving schemas, and connectivity or authentication problems with upstream systems.
• Contribute to shared operational ownership by creating documentation, runbooks, and support procedures that strengthen team coverage and reduce dependency on any one individual.
• Produce clear technical documentation for pipeline design, platform operations, and maintenance practices to support long-term scalability and team collaboration.
Qualifications:
• 3+ years of practical data engineering experience supporting production-grade data platforms.• Strong hands-on expertise with Databricks, including notebooks, jobs, workflows, cluster configuration, SQL warehouses, and Unity Catalog administration.
• Proven ability to develop and troubleshoot distributed data transformations using Python, PySpark, and SQL.
• Solid experience working with Microsoft SQL Server, including database tuning, indexing, schema management, and query performance analysis.
• Background in Apache Airflow for building, scheduling, and debugging DAG-driven workflows in live environments.
• Experience integrating data from SaaS or operational source systems through batch, bulk, or streaming APIs.
• Ability to monitor, support, and troubleshoot active data pipelines with a strong focus on reliability and issue resolution.
• Strong written and verbal communication skills, including the ability to create clear and maintainable technical documentation.
Compensation
$130,000.00-$150,000.00 YearlyAbout Us
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