Sr Data Engineer

Flexstaf IT; Irvine (CA); Ibrido; USD 120.000 - 190.000; Tempo pieno

Descrizione del lavoro

Flexstaf IT is seeking a Senior Data Engineer to design and implement a modern data platform supporting enterprise analytics, integration, and AI/ML initiatives. You will help build an enterprise data warehouse, enable visualization tools, and promote self-service analytics for business users.

The role requires 5+ years in data engineering, expertise with Snowflake, dbt, Fivetran, and strong SQL/Python skills.

Competenze

• Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related technical field.
• 5+ years of professional experience in data engineering or related roles.
• Proven expertise with Snowflake for data pipelines, development, and optimization.
• Strong experience with dbt for modular transformations and Fivetran for automated ingestion.
• Solid background in data warehousing, dimensional modeling, and performance tuning.
• Hands-on experience with AWS cloud services (especially S3) for data integration and storage.
• Familiarity with data governance tools (Alation or Atlan) for cataloging, lineage, and stewardship.
• Proficiency in SQL and comfort with scripting languages such as Python.

Mansioni

• Data Engineering & Pipeline Development – Design, build, and maintain scalable data pipelines and workflows in Snowflake, leveraging dbt for modular transformations and Fivetran for automated ingestion.
• Enterprise Data Warehouse & Modeling – Architect and optimize the enterprise data warehouse, applying best practices in data modeling, performance tuning, and scalability to support enterprise-wide analytics.
• Data Governance & Quality – Partner with governance teams to document cataloging, lineage, and stewardship, while ensuring data quality, security, and compliance.
• Collaboration & Business Enablement – Work closely with business and analytics teams to translate requirements into scalable data solutions.
• Cloud Integration & Infrastructure – Utilize AWS services (primarily S3) for data storage and integration, ensuring reliability, performance, and cost-effectiveness.

Conoscenze

Snowflake

dbt

Fivetran

Data Warehousing

SQL

Python

AWS

Data Governance

Communication

AI/ML awareness

Formazione

Bachelor's degree in Computer Science, Data Engineering, Information Systems, or related field

Master's degree (Preferred)

Descrizione del lavoro

Location: Irvine, CA (3 days work from office per week)

Duration: CTH

Purpose of the job:

The Senior Data Engineer will be part of a team designing and implementing our modern data platform to support enterprise-wide analytics, integration, and AI/ML initiatives. Together, the team will build a scalable enterprise data warehouse, enable modern visualization tools, and advance self-service analytics capabilities for business users.

Major Duties and Responsibilities:

• Data Engineering & Pipeline Development – Design, build, and maintain scalable data pipelines and workflows in Snowflake, leveraging dbt for modular transformations and Fivetran for automated ingestion.
• Enterprise Data Warehouse & Modeling – Architect and optimize the enterprise data warehouse, applying best practices in data modeling, performance tuning, and scalability to support enterprise-wide analytics.
• Data Governance & Quality – Partner with governance teams to document cataloging, lineage, and stewardship, while ensuring data quality, security, and compliance.
• Collaboration & Business Enablement – Work closely with business and analytics teams to translate requirements into scalable data solutions.
• Cloud Integration & Infrastructure – Utilize AWS services (primarily S3) for data storage and integration, ensuring reliability, performance, and cost-effectiveness.

Education and Experience:

Required:

• Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related technical field.
• 5+ years of professional experience in data engineering or related roles.
• Proven expertise with Snowflake for data pipelines, development, and optimization.
• Strong experience with dbt for modular transformations and Fivetran for automated ingestion.
• Solid background in data warehousing, dimensional modeling, and performance tuning.
• Hands-on experience with AWS cloud services (especially S3) for data integration and storage.
• Familiarity with data governance tools (Alation or Atlan) for cataloging, lineage, and stewardship.
• Proficiency in SQL and comfort with scripting languages such as Python.

Preferred:

• Master's degree in a related field.
• Experience with real-time/streaming technologies (e.g., Kafka, Kinesis, Flink).
• Knowledge of data security, compliance, and access control best practices.
• Exposure to AI/ML workflows and integrating data engineering solutions with ML pipelines.
• Background in modern visualization platforms (Power BI, Tableau, Looker) to support self-service analytics.
• Demonstrated ability to A39mentor junior engineers and contribute to engineering best practices.

Knowledge and skill level:

• Snowflake (Expert) – deep expertise in data pipelines, query optimization, and enterprise-scale implementation.
• dbt (Advanced) – strong ability to design modular, reusable transformations and maintain version-controlled models.
• Fivetran (Advanced) – hands-on skill in managing ingestion pipelines and troubleshooting data connectors.
• Data Warehousing & Data Modeling (Advanced) – proficiency in dimensional modeling, star/snowflake schemas, and performance tuning.
• AWS Cloud (Intermediate) – working knowledge of S3 for storage and integration; familiarity with broader AWS data services a plus.
• Data Governance (Intermediate) – understanding of metadata management, lineage, and cataloging.
• SQL (Expert) – strong command of writing complex queries and optimizing performance.
• Python (Intermediate) – ability to script automation, testing, and lightweight data transformations.
• Collaboration & Communication – skilled in working with cross-functional teams and translating technical concepts to business stakeholders.
• Emerging Tech & AI – awareness of how AI/ML can accelerate data engineering practices; willingness to experiment and learn.