Data Architect

Syren Technologies; United States; Remote; USD 150,000 - 210,000; Full time

Benefits offered by this job

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Job summary

Syren Technologies is seeking an experienced Data Architect to design scalable data architectures on Databricks across Azure, AWS, or GCP. You will define end-to-end lakehouse architectures and set engineering standards to ensure secure, cost-efficient data pipelines.

The role requires hands-on Databricks, Delta Lake, Unity Catalog, and Medallion Architecture knowledge, plus strong SQL, Python, PySpark, and Spark skills. Collaboration with cloud teams and stakeholders is essential.

Qualifications

• Requires strong data architecture and enterprise data engineering experience.
• Hands-on Databricks and cloud-based data platform experience.
• Expertise in at least one cloud platform (Azure, AWS, or GCP).
• Solid understanding of lakehouse, Delta Lake, Medallion Architecture.
• Proficiency in SQL and Python, PySpark, Spark.
• Ability to translate requirements into scalable architectures.

Responsibilities

• Design and implement scalable, secure, and cost-effective data architectures using Databricks on Azure, AWS, or GCP.
• Define end-to-end lakehouse architecture, including data ingestion, transformation, storage, processing, and consumption layers.
• Develop architecture standards and best practices for data engineering, data modelling, data integration, and platform design.
• Leverage Databricks capabilities, including Delta Lake, Unity Catalog, and the Medallion Architecture, wherever appropriate.
• Design and optimize batch and streaming data pipelines using Apache Spark, PySpark, SQL, and related technologies.
• Establish data governance, security, access control, data quality, metadata management, and lineage practices.
• Collaborate with data engineers, analytics teams, business stakeholders, and cloud/platform teams to deliver reliable data solutions.
• Identify and resolve architectural challenges related to scalability, performance, reliability, and cloud cost optimization.
• Evaluate existing data platforms and recommend modernization, migration, and optimization strategies.
• Document architecture designs, technical standards, data flows, and implementation guidelines.

Skills

Databricks

Cloud platforms

SQL

Python

PySpark

Apache Spark

Data modelling

Data integration

Data governance

Problem solving

Tools

Delta Lake

Unity Catalog

Medallion Architecture

Apache Spark

Job description

Role & responsibilities:

• Design and implement scalable, secure, and cost-effective data architectures using Databricks on Azure, AWS, or GCP.
• Define end-to-end lakehouse architecture, including data ingestion, transformation, storage, processing, and consumption layers.
• Develop architecture standards and best practices for data engineering, data modelling, data integration, and platform design.
• Leverage Databricks capabilities, including Delta Lake, Unity Catalog, and the Medallion Architecture, wherever appropriate.
• Design and optimize batch and streaming data pipelines using Apache Spark, PySpark, SQL, and related technologies.
• Establish data governance, security, access control, data quality, metadata management, and lineage practices.
• Collaborate with data engineers, analytics teams, business stakeholders, and cloud/platform teams to deliver reliable data solutions.
• Identify and resolve architectural challenges related to scalability, performance, reliability, and cloud cost optimization.
• Evaluate existing data platforms and recommend modernization, migration, and optimization strategies.
• Document architecture designs, technical standards, data flows, and implementation guidelines.

Preferred candidate profile

• Strong experience in data architecture, data platform design, or enterprise data engineering.
• Hands-on experience with Databricks and cloud-based data platforms.
• Strong expertise in at least one cloud platform: Microsoft Azure, AWS, or GCP.
• Solid understanding of lakehouse architecture, Delta Lake, and the Medallion Architecture.
• Proficiency in SQL and practical knowledge of Python, PySpark, and Apache Spark.
• Experience designing data models, data pipelines, and data integration solutions.
• Understanding of data governance, security, access management, data quality, and metadata management.
• Experience with performance tuning, scalability, reliability, and cost optimization of data platforms.
• Ability to translate business and technical requirements into scalable architectural solutions.
• Strong communication, problem-solving, stakeholder management, and technical documentation skills.