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A data solutions company is seeking a Senior Data Engineer to lead the migration from Redshift to Snowflake. The ideal candidate will have expertise in Snowflake, DBT, and data observability tools like Splunk and OpsGenie. Responsibilities include executing the transition, ensuring data governance, and driving project goals for 2026. This role requires high initiative, strong execution focus, and the ability to clearly document processes. The position is remote, available within the LATAM region.
Senior Data Engineer (Snowflake & Observability Implementation) Location : LATAM [Remote]
6 Months
Need to work on PST hours
We are seeking a proactive and hands-on Senior Data Engineer to drive the execution phase of our migration from Amazon Redshift to Snowflake. The architectural design is complete; we need a builder who can code, implement, and operationalize the solution with a heavy focus on Good Governance, Data Quality, and Observability at scale.
You will pick up where a previous engagement left off, leveraging DBT for transformations and driving the roadmap for 2026. You will work alongside an existing Data Engineer but are expected to lead the implementation with high initiative.
Lead the Implementation: Execute the transition from Redshift to Snowflake based on existing designs.
DBT Transformation: Utilize DBT to manage transformations for DMS outputs and ensure seamless data integration.
Code & Drive: This is not a passive role. You will be responsible for writing the code, configuring the environment, and driving the project forward to meet 2026 roadmap goals.
Measure Output: Implement metrics to validate and measure the output of DMS processes to ensure accuracy.
Enterprise Alerting Integration: Operationalize a Proof of Concept (POC) that connects Splunk to OpsGenie.
Scale Observability: Deploy observability checks (Freshness, Validity, Volume) across all tables in the ecosystem.
Incident Routing: Configure automated workflows to ensure alerts reach the correct contact immediately via OpsGenie.
Data Governance: Enforce “Good Governance” practices, including rigorous Data Quality (DQ) checks and comprehensive documentation.
Change Management: Manage the change process for data pipelines and schema evolution.
Advanced DQ Tooling: Operationalize a DQ POC using Atlan, specifically leveraging its natural language capabilities for data discovery and quality management.