Senior Data Engineer

Kula Example; San Francisco (CA); On-site; USD 190,000 - 230,000; Full time

Job summary

Kula Example in San Francisco seeks an experienced Senior Data Engineer to design and maintain robust data pipelines and warehouses, powering BI and ML initiatives.

You will collaborate with analysts and data scientists, implement data quality checks, and partner with engineers to integrate data assets across systems. The role demands 4+ years in data engineering and strong SQL/Python, dbt, Snowflake or BigQuery, Airflow, Docker, and CI/CD, plus a degree in CS.

Qualifications

• 4+ years of experience in data engineering.
• Proficiency in SQL and Python.
• Experience with dbt, Snowflake or BigQuery, Airflow, Docker, and CI/CD for data workflows.
• Bachelor’s degree in Computer Science or equivalent.

Responsibilities

• Build and maintain production-ready data pipelines and warehouses.
• Implement data quality checks and monitoring for data accuracy.
• Collaborate with analysts and data scientists to deliver high-quality datasets.
• Design scalable data architectures with cross-functional teams.
• Document pipelines and systems for clarity and maintainability.
• Support on-call rotations and resolve data-related incidents.

Skills

SQL

Python

dbt

Snowflake

BigQuery

Airflow

Docker

CI/CD

Data pipelines

Data quality

Kubernetes

Education

Bachelor's degree in Computer Science

Tools

Snowflake

BigQuery

Airflow

Docker

Job description

About

We are seeking an experienced Senior Data Engineer to join our team at showcase. As a key member of our data team, you will play a vital role in building and maintaining our robust data infrastructure, powering business intelligence and machine learning initiatives.

Requirements

4+ years of experience in data engineering, with a strong proficiency in SQL, Python, dbt, and Snowflake or BigQuery. You should also have hands‑on experience with Airflow, Docker, and CI/CD for data workflows. A Bachelor’s degree in Computer Science or an equivalent field is required.

Responsibilities

As a Senior Data Engineer, you will be responsible for building and maintaining production-ready data pipelines and warehouses. You will implement data quality checks and monitoring to ensure the accuracy and reliability of our data. You will also partner with analysts and data scientists to deliver high-quality datasets at scale, driving business insights and informed decision-making.

Key Responsibilities and Deliverables

• Collaborate with cross‑functional teams, including data analysts, data scientists, and product managers, to design and develop scalable data architectures and solutions.
• Build and maintain data pipelines and warehouses using industry‑leading tools and technologies, such as Snowflake, BigQuery, and dbt.
• Implement data quality checks and monitoring to ensure data accuracy, consistency, and reliability.
• Partner with analysts and data scientists to deliver high‑quality datasets at scale, driving business insights and informed decision‑making.
• Work closely with engineering teams to integrate data pipelines and services with existing applications and systems.
• Develop and maintain documentation for data pipelines and systems, ensuring clarity and consistency across the team.
• Collaborate with the data governance team to ensure compliance with data security, privacy, and regulatory requirements.
• Stay up‑to‑date with emerging trends and technologies in data engineering, and make recommendations for tooling and process improvements.
• Mentor junior data engineers, sharing knowledge and best practices to drive team growth and success.
• Participate in on‑call rotations, providing support and troubleshooting for data‑related incidents and outages.
• Drive continuous improvement in data engineering practices, identifying areas for optimization and implementing process changes as needed.

Skills

• Strong proficiency in SQL and Python programming languages.
• Experience with data engineering tools, such as dbt, Snowflake, BigQuery, Airflow, Docker, and CI/CD.
• Excellent problem‑solving and analytical skills, with the ability to troubleshoot complex data‑related issues.
• Strong communication and collaboration skills, with the ability to work effectively with cross‑functional teams.
• Experience with data governance and compliance requirements, including data security, privacy, and regulatory standards.
• Strong understanding of data engineering principles and practices, including data modeling, data warehousing, and data integration.
• Experience with cloud‑based data platforms, such as Snowflake or BigQuery.
• Strong understanding of data quality and data monitoring techniques, including data profiling and data validation.
• Experience with data visualization tools, such as Tableau or Power BI.
• Strong understanding of data engineering best practices, including modular design, scalability, and maintainability.
• Experience with containerization and orchestration tools, such as Docker and Kubernetes.
• Strong understanding of data storage and retrieval techniques, including data warehousing and data lakes.
• Experience with data quality and data cleansing techniques, including data profiling and data validation.
• Strong understanding of data governance and compliance requirements, including data security, privacy, and regulatory standards.