Data Infrastructure Architect
J
JobgetherData Infrastructure
Based in United StatesFull-TimeSenior
Salary175,000 - 200,000 USD per year
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Job Details
- Experience
- 8+ years
- Required Skills
- SnowflakeApache KafkaSparkData modelingBigQueryRedshiftDatabricks
Requirements
- Bachelor’s or Master’s degree in Computer Science, Information Systems, or a related technical field.
- 8+ years of experience in data engineering, including significant experience in architecture-focused roles.
- Deep expertise with major data platforms such as Snowflake, Databricks, BigQuery, or Redshift.
- Strong understanding of lakehouse architectures, modern table formats, and large-scale streaming systems.
- Hands-on production experience with technologies such as Spark, Flink, or Kafka.
- Strong expertise in data modeling methodologies, including dimensional, normalized, and data-vault approaches.
- Experience implementing data governance, lineage, metadata management, and catalog capabilities.
- Solid understanding of cloud platforms, networking concepts, identity management, and cost optimization strategies.
- Proven experience leading complex data platform programs across multiple teams and stakeholders.
- Excellent communication, facilitation, and stakeholder management skills.
Responsibilities
- Define and lead the architecture of enterprise data platforms across ingestion, storage, processing, governance, and consumption layers.
- Establish data architecture standards, patterns, and best practices to support scalable and maintainable solutions.
- Design and evolve modern data platform architectures, including lakehouse environments, streaming systems, and cloud-based data solutions.
- Partner with data engineering, analytics, machine learning, and business teams to deliver a cohesive and effective data foundation.
- Guide the selection, implementation, and optimization of enterprise data technologies and platforms.
- Develop and maintain data modeling strategies across dimensional, normalized, and data-vault approaches.
- Drive implementation of data governance frameworks, including lineage, cataloging, security, and lifecycle management.
- Provide architectural guidance for data processing solutions using technologies such as Spark, Flink, and Kafka.
- Support cloud architecture decisions involving infrastructure, networking, identity management, security, and cost optimization.
- Lead large-scale data platform initiatives across multiple teams, ensuring alignment with business objectives and technical standards.
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