Senior Data Engineer

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.