Data Engineer

Saxon Global•Atlanta, GA

17d

About The Position

We are seeking a Senior Data Engineer to build, operate, and improve an enterprise data warehouse and production data pipelines in an Azure Databricks environment. This hands-on role will work across the Bronze, Silver, and Gold layers of a medallion architecture, with particular emphasis on data ingestion, transformation, modeling, quality, and reliability. The Senior Data Engineer will own assigned data domains as production systems, help simplify legacy transformation layers, and support the transition from batch processing to event-driven, low-latency ingestion. This person will also establish strong engineering practices, document complex systems, review code, and mentor other Data Engineers.

Requirements

Six or more years of progressive data engineering experience.
Production ownership of an enterprise data warehouse or large-scale data transformation pipelines governed by defined SLAs.
Deep, hands-on Databricks experience in production environments.
Strong experience with Delta Lake, medallion architecture, Unity Catalog, and Databricks pipeline optimization.
Advanced SQL skills and strong proficiency with Python and PySpark.
Ability to independently build, review, troubleshoot, and optimize complex data transformations.
Strong data warehousing experience, including dimensional modeling, harmonized data models, conformance rules, and survivorship logic.
Hands-on experience with Azure Databricks, Azure Data Lake Storage, Azure Data Factory, and CI/CD practices using Azure DevOps.
Experience operating data pipelines as production systems, including monitoring, incident response, reliability, and performance management.
Ability to learn complex, under-documented data environments and convert that knowledge into clear, durable technical documentation.
Strong communication skills and the ability to collaborate directly with technical, governance, quality, analytics, and business stakeholders.

Nice To Haves

Experience in healthcare, pharmacy, specialty pharmacy, or another regulated industry involving clinical or operational data. (Strongly desired)
Production experience with event-driven ingestion using Azure Event Hubs, Kafka, or equivalent technology through Structured Streaming.
Familiarity with Zerobus Ingestion and Spark Declarative Pipelines.
Experience implementing data contracts using ODCS or an equivalent standard.
Familiarity with pipeline-observability platforms such as Monte Carlo.
Experience with enterprise data-catalog platforms such as Atlan, Collibra, or equivalent tools.
Experience creating catalogued and governed data-pipeline assets with documented metadata and lineage.
Demonstrated technical leadership through mentoring, code review, standards development, and influence without formal management authority.
Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related discipline, or equivalent professional experience.

Responsibilities

Design, build, and support enterprise data warehouse pipelines and data models across Bronze, Silver, and Gold layers in Databricks.
Develop ingestion patterns into the Bronze layer and transformation logic that produces standardized, reliable Silver-layer data.
Create dimensional and harmonized data models, including conformance, survivorship, and Gold-layer promotion rules.
Take production ownership of assigned data domains, including model integrity, pipeline reliability, ingestion SLAs, incident response, and root-cause remediation.
Build and maintain Delta Lake pipelines while helping transition selected workloads from batch processing to event-driven, low-latency ingestion.
Use Zerobus Ingestion, Spark Declarative Pipelines, or supported event-streaming patterns for high-SLA data sources.
Implement row-level reconciliation, validation checkpoints, automated quality gates, and data-contract validation throughout the data lifecycle.
Build Gold-layer tables that support Unity Catalog metric views, lineage tracking, governed access, and downstream analytics.
Optimize pipeline performance, processing latency, reliability, and Databricks compute costs.
Consolidate legacy transformation layers into a simplified target architecture while validating that outputs remain accurate.
Monitor pipeline health, investigate anomalies, and resolve data-reliability incidents.
Document architecture decisions, data models, transformation logic, feed SQL, and operational procedures.
Review pull requests and maintain code quality through Azure DevOps and Git-based CI/CD practices.
Mentor Data Engineers in Databricks development, SQL, Python, PySpark, testing, and documentation.
Collaborate directly with data governance, quality assurance, analytics engineering, and business-facing teams to resolve requirements and delivery issues.