26-223: Senior Data Engineer

Overview:
We are seeking an experienced Data Engineer with strong hands-on expertise in Amazon Redshift, SQL development, ETL/ELT pipelines, and production data engineering support.
The primary responsibility of this role is to develop, maintain, monitor, troubleshoot, and optimize our existing Amazon redshift data warehouse and associated data integration workflows. The engineer will ensure the reliability performance accuracy and timely execution of scheduled data pipelines supporting enterprise analytics and reporting.
The ideal candidate will have experience with complex SQL development, ETL process design, job scheduling, production monitoring, performance tuning, and troubleshooting. Familiarity with tableau and its underlying data dependencies is important.
As part of a broader data and AI platform modernization strategy the engineer will also participate in the phased migration of existing redshift workloads to Databricks. The successful candidates must demonstrate a willingness and ability to learn Databricks, Delta Lake, and modern data engineering practices.
Key Responsibilities
Production Monitoring, Troubleshooting, and Support

• Monitor scheduled ETL jobs, Redshift workloads, data pipelines, and production data processing activities.
• Proactively identify and resolve job failures, performance bottlenecks, data discrepancies, and scheduling issues
•  Rapidly diagnose, debug, and resolve production issues involving SQL, ETL workflows, and scheduled automation.
• Perform root-cause analysis of recurring production incidents and implement permanent corrective solutions.
• Monitor data pipeline execution, processing times, failures, and system performance to ensure operational reliability.
• Develop and maintain monitoring scripts, automated alerts, and operational dashboards for production workflows.
• Participate in rotational on-call production support to troubleshoot and resolve critical data processing issues.
• Maintain operational documentation, incident resolution procedures, and production support runbooks.
• Collaborate with infrastructure, analytics, and other IT teams to resolve production issues and maintain data availability.

Redshift and ETL/ELT Pipeline Design, Development, and Automation 

• Design, develop, deploy, and maintain data warehouse solutions using Amazon Redshift.
• Perform SQL performance tuning, query optimization, and troubleshooting to improve data processing efficiency
• Analyze existing data models, schemas, tables, and transformation logic to identify performance and reliability improvements.
• Develop and maintain scalable ETL/ELT solutions supporting enterprise data integration and analytics.
• Work with Amazon S3, Python, and other AWS data services to support data processing and integration requirements.
• Develop and maintain Python scripts and SQL-based data transformation solutions.
• Maintain scheduled data processing jobs, including job dependencies, execution sequences, and automated recovery mechanisms.
• Improve existing ETL solutions through SQL optimization, automation, and reusable engineering practices.
• Implement error handling, retry mechanisms, logging, and alerts to improve pipeline reliability.
• Work with enterprise source systems, including PeopleSoft, D2L, Salesforce, and other structured data sources as required.

Data Quality, Validation, and Analytics Support

• Develop data validation checks, reconciliation processes, and automated quality controls to ensure data accuracy and integrity.
• Investigate data discrepancies and resolve issues between source systems, Redshift transformations, and downstream reporting.
• Work with analytics teams to understand business logic, data requirements, and reporting dependencies.
• Support Tableau reporting and analytics workloads by ensuring the accuracy, availability, and performance of underlying Redshift data.
• Troubleshoot SQL queries, data models, and pipeline failures impacting Tableau dashboards and scheduled reporting.
• Assist with data model enhancements, reporting performance optimization, and resolution of data-related incidents.
• Validate that data transformations and business rules are correctly implemented.

Redshift to Databricks Migration and Modernization

• Support the organization’s phased transition from Amazon Redshift to Databricks.
• Assess existing Redshift schemas, SQL workloads, ETL processes, data marts, and reporting dependencies to support migration planning.
• Develop an understanding of the Databricks Lakehouse architecture, Apache Spark, Delta Lake, and the Medallion Architecture (Bronze, Silver, and Gold layers).
• Learn and adopt Databricks engineering tools, including Databricks SQL, PySpark, Workflows, and Unity Catalog.
• Participate in converting existing Redshift SQL transformations and ETL processes into Databricks-compatible solutions.
• Support data reconciliation, functional testing, performance validation, and production readiness during migration.
• Help maintain operational stability while Redshift and Databricks coexist during the transition.
• Develop hands-on Databricks expertise through training, collaboration, and participation in modernization initiatives.
• Adopt Databricks governance capabilities, including Unity Catalog, as the platform evolves.

Documentation, Collaboration, and Accountability

• Maintain technical documentation covering SQL transformations, ETL pipelines, scheduling dependencies, architecture, and production support procedures.
• Collaborate with analytics, infrastructure, and application teams to prioritize and resolve data engineering issues.
• Conduct code reviews, promote reusable engineering practices, and support knowledge sharing.
• Provide regular progress updates covering completed work, production incidents, upcoming activities, and blockers.
• Track assigned deliverables and proactively communicate risks, dependencies, and operational concerns.

Required Qualifications

• Strong hands-on experience in Amazon Redshift data warehousing, SQL development, and production support.
• Experience participating in rotational on-call support for production systems.
• Demonstrated expertise in writing, debugging, tuning, and deploying complex SQL queries and data transformations.
• Experience with production job monitoring, scheduled automation, troubleshooting, and incident resolution.
• Strong understanding of SQL development, unit testing, data validation, and production deployment practices.
• Experience developing Python scripts for ETL automation, data processing, monitoring, and operational utilities.
• Familiarity with Amazon S3 and AWS-based data integration workflows.
• Experience supporting data warehouse workloads consumed by Tableau or similar business intelligence platforms.
• Ability to analyze and troubleshoot data pipeline failures, SQL performance problems, and downstream reporting issues.
• Demonstrated willingness and ability to learn Databricks and participate in Redshift-to-Databricks modernization activities.