Data Engineer

You might be our missing piece if you have:

• 3+ years of experience in data engineering, database development, and cloud-based data solutions

• A degree in a technical field (e.g., Computer Science, Engineering) or equivalent professional experience

• Strong proficiency in SQL and database technologies (e.g., SQL Server, PostgreSQL)

• Proficiency in Python and/or PySpark, with the ability to write clean, efficient, and reusable code for data processing and transformation.

• Hands-on experience with ETL/orchestration tools and frameworks (e.g., Azure Data Factory, Azure Databricks, Apache Airflow, or equivalent).

• Cloud experience with major providers (Azure and/or AWS)

• Experience with data modeling, data integration, and data warehousing concepts

• Knowledge of infrastructure as code

• Knowledge of data governance, quality frameworks, and security best practices

• Experience working with large, diverse datasets and building scalable data solutions.

We would be thrilled if you have:

• Experience implementing data governance and quality frameworks

• Exposure to event-driven or real-time data architectures (e.g., Kafka, Kinesis, Event Hubs)

• Familiarity with machine learning pipelines, predictive modeling, or NLP concepts

• Knowledge of modern data stack tools (e.g., dbt, Snowflake, Redshift, Databricks SQL)

• A passion for continuous improvement, innovation, and driving best practices in data engineering

• A sense of belonging while reading about our culture.

We will be working together on:

• Designing, developing, and maintaining data pipelines and ETLs that ensure reliability, scalability, and discoverability

• Building and maintaining ETL/orchestration workflows and troubleshooting data pipeline issues

• Operating within cloud environments (Azure and/or AWS), managing and integrating cloud services effectively

• Developing infrastructure-as-code scripts for consistent, scalable data environments

• Supporting data governance and quality frameworks to maintain data integrity and compliance

• Implementing automated testing (unit, integration, end-to-end, contract) to maintain data reliability.

• Providing technical guidance on data integration, transformation, and analytics, and building dashboards for product teams

• Collaborating with cross-functional teams to design and deliver solutions that meet both business and technical needs.