Must Have : Python, SQL, Snowflake, ETL, Data Engineering, Airflow, Kafka

Experience : 5 to 8 Years

As a Senior Data Engineer on the Enterprise Data Platform team, you will be responsible for building data pipelines to ingest various source data from enterprise technologies Platform data, manipulating (cleanse, dedupe, normalize) data into well-constructed data models for data analysis, implementing business logic and standard calculations, governing (supporting, observing, documenting) the data, and making the data available to end consumers in the form of data products built on top of our data warehouse/data lake (e.g. Snowflake). You'll work with a range of data and reporting technologies (e.g. Python, Docker, Tableau, Power BI) to build upon a strong foundation of rigor, quantitative techniques, and efficient processing. You'll join other Engineers and Analytics professionals as part of the team that develops data pipelines and insights for our internal stakeholders across Sales, Customer Success, Marketing, Research, Data Operations, Product, Finance, and Administration.

Your team will rely on you to build your skills in data techniques and analytics to deliver accurate, timely, accessible, and secure data & insights to users. You'll collaborate closely and effectively with internal and external stakeholders of different roles and technical backgrounds, who have varying understanding of data engineering. You'll have the opportunity and ability to impact many different areas of analytics and operational thinking across enterprise technology and product engineering.

You will exhibit a growth mindset, be willing to solicit feedback, engage others with empathy, and help create a culture of belonging, teamwork, and purpose. If you love building data-centric solutions, strive for excellence every day, are adaptable and focused, and believe work should be fun, come join us!

Primary Job Responsibilities :

- Apply unified data technologies to support advanced and automated business analytics

- Design, develop, document, and maintain database and reporting structures used to compile insights

- Define, develop, and review extract, load, and transform (ELT) processes and data modeling solutions

- Consistently evolve data processes and techniques following industry best practices

- Build data models to be used for reports and dashboards used to translate business data into insights, identify and prioritize operational improvement opportunities, and measure business KPIs against objectives

- Contribute to the ongoing improvement of quality assurance standards and procedures

- Support the vision and values of the company through role modeling and encouraging desired behaviors

- Participate in various company initiatives and projects as requested

Skills and Qualifications :

- Bachelor's degree in a related field (Computer Science, Engineering, etc.)

- 5+ years of experience in data engineering roles, including creating and maintaining data pipelines, data modeling, and data architecture

- 5+ years of experience in advanced SQL, including expert-level skills in querying large datasets from multiple sources and developing automated reporting

- 3+ years of experience in Python, with skills for diverse components of data pipelines, including scripting, data manipulation, custom extract, transform and loads, and statistical/regression analysis

- Expertise in extract, transform, and load (ETL) and extract, load, transform (ELT) processes and pipelines, platforms (e.g. Airflow), and distributed messaging (e.g. Kafka)

- Experience with tools that capture and control data modeling change management (e.g. SQLMesh)

- Proficient in data storage solutions, data warehousing, and cloud-based data platforms (e.g. Snowflake)

- Knowledge and applicable working experience establishing and ensuring data governance, data quality, and compliance standards

- Exceptional problem-solving skills

- Excellent communication and collaboration skills with the ability to engage with non-technical stakeholders

- Experience working with enterprise technologies (CRM, ERP, Marketing Automation Platforms, Financial Systems, etc.) is a plus