Data Engineer

Suzega; United States; Remote; USD 120,000 - 170,000; Full time

Job summary

Suzega seeks an autonomous data engineer to lead end-to-end data modernization on Microsoft Azure, turning client requirements into scalable, governed data pipelines that fuel analytics and AI workloads. The role centers on Azure Synapse Dedicated SQL Pools and Fabric-like data hubs, preparing data for downstream ML and NLP use cases.

You will own the full lifecycle from ingestion to optimization, build robust ETL/ELT pipelines, and collaborate with AI/ML engineers to ground LLM applications in

Qualifications

• Deep expertise in T-SQL and Azure Synapse Dedicated SQL Pools.
• Experience with Azure Data Hub and data warehouse scale.
• Strong Python for data manipulation and AI orchestration.
• Familiarity with AI integration patterns, vector databases, and semantic layers.
• Solid data warehousing concepts and dimensional modeling.

Responsibilities

• Design, build, and tune ETL/ELT pipelines using T-SQL and Python across Synapse and Azure Data Hub.
• Develop medallion-style layered schemas (Bronze, Silver, Gold) for analytics and AI readiness.
• Embed AI capabilities into data ops, including automated incident triage and cost-saving recommendations.
• Implement data quality checks, observability, and governance across warehouses, lakes, and stores.
• Orchestrate workflows with Airflow/Prefect/Dagster and collaborate with AI/ML engineers for LLM-grounded data.

Skills

T-SQL

Python

PySpark

Pandas

AI orchestration

Tools

Azure Synapse Dedicated SQL Pools

Azure Data Hub

Azure Data Lake Storage Gen2

Airflow

Prefect

Dagster

Job description

Role overview

This position leads end-to-end data modernization on Microsoft Azure, turning raw client requirements into scalable, governed data pipelines that fuel analytics and AI workloads. The work centers on Azure Synapse Dedicated SQL Pools and Fabric-style data hubs, with a strong emphasis on preparing data for downstream machine learning and natural-language search use cases. It suits an autonomous engineer who enjoys owning the full lifecycle from ingestion through optimization.

Responsibilities

• Design, build, and tune ETL/ELT pipelines using advanced T-SQL and Python across Synapse Dedicated SQL Pools and Azure Data Hub environments.
• Develop medallion-style layered schemas (Bronze, Silver, Gold) tailored for high-performance analytics and AI-ready consumption.
• Embed AI capabilities into data operations, including automated incident triage, cost-saving recommendations, and proactive pipeline monitoring.
• Implement data quality checks, observability frameworks, and governance controls across warehouses, lakes, and SQL/NoSQL stores.
• Orchestrate workflows with tools such as Airflow, Prefect, or Dagster, and partner with AI/ML engineers to ground LLM applications in trusted data through vector embeddings and semantic metadata.
• Optimize storage and compute spend across the Azure stack using FinOps automation and workload tuning, while troubleshooting issues independently.

Requirements

• Deep expertise in T-SQL and hands-on experience with Azure Synapse Dedicated SQL Pools, Azure Data Hub, and relational/NoSQL databases.
• Strong Python proficiency for data manipulation, pipeline development, and AI orchestration, including libraries like PySpark and Pandas.
• Practical knowledge of the Azure data stack, including Synapse Pipelines and ADLS Gen2, at enterprise data-warehouse scale.
• Familiarity with AI integration patterns such as prompt engineering, vector databases, and semantic layers for natural-language querying.
• Solid grasp of data warehousing concepts, dimensional modeling, and data quality principles.
• Ability to work independently, take ownership of infrastructure components, and uphold data security and compliance standards.