Senior Data Engineer

As a Senior Data Engineer at Paradigm, your primary responsibilities will include partnering with product managers, data scientists, and analysts to create elegant technical solutions for complex business problems. You'll be building robust batch and streaming data pipelines and ensuring that the data is accessible and reliable across various consumer products. Additionally, responsibilities include evolving monitoring systems for better visibility, acting as a subject matter expert on datasets, and collaborating with privacy teams to govern data responsibly.

To qualify for the Senior Data Engineer role at Paradigm, candidates should have a minimum of 5 years of experience in data engineering. Key qualifications include proficiency in SQL, expertise in data workflow optimization, coding skills in Python or similar languages, and experience with ETL tools such as Apache Airflow. Familiarity with data transformation tools and a background in healthcare data are desirable but not mandatory. Strong analytical skills and the ability to drive technical projects are essential.

In the Senior Data Engineer role at Paradigm, you'll work with various technologies including SQL for data analysis, Apache Airflow for ETL orchestration, and data transformation tools like dbt. Experience with data warehouses such as BigQuery, Redshift, or Snowflake is also part of the role. You will design systems that are scalable and maintainable to handle clinical care data effectively.

As a Senior Data Engineer, you contribute significantly by developing the infrastructure that supports data ingestion and harmonization from multiple sources. This enables other departments, like data scientists and analysts, to derive insights for optimizing patient trial matching and study conduct, ultimately improving the overall clinical trial process. Your work ensures that the infrastructure is robust, reliable, and geared towards making clinical trials accessible to all patients.

Paradigm promotes a diverse, inclusive, and collaborative remote work environment. As a Senior Data Engineer, you'll work closely with a team of passionate professionals dedicated to making a difference in clinical research. The culture encourages creativity and innovation, allowing you to contribute to meaningful projects while gaining insights from a variety of perspectives within the team.

When designing data pipelines, I focus on understanding the business requirements first. Then, I ensure the pipeline is robust by selecting the right architectures, optimizing SQL queries for speed, and leveraging ETL tools effectively. I always aim for scalability that can handle increased data volume and complexity as needed.

One challenging project involved integrating disparate data sources into a single, unified system. I navigated complexities around data formats and ensured compliance with privacy regulations. The experience taught me the importance of thorough testing and monitoring, which I applied to exceed the project requirements and ensure a seamless rollout.

For monitoring data quality, I implement automated validation checks at various stages of the data pipeline to identify anomalies. I also establish comprehensive logging mechanisms to track data lineage. Regular audits and employing tools that visualize data distributions are crucial for maintaining high data quality over time.

I prioritize addressing technical debt by allocating time in sprints for refactoring and optimizing existing code. I maintain clear documentation and strive to write elegant, maintainable code to minimize future debts. Regular code reviews and collaborating with the team on improvement suggestions also play a key role.

I have extensive experience using data transformation tools like dbt. I value how they allow for modular SQL development and facilitate easier testing and documentation. My approach has been to maintain clean transformation logic, enabling the team to iterate quickly while ensuring data accuracy.

To ensure data security, I work closely with privacy teams to implement best practices, such as encryption and limited data access. Continuous education on compliance standards is essential. I also implement user authentication and conduct regular security audits to identify and mitigate potential vulnerabilities.

I believe in open communication and setting clear expectations at the outset of projects when collaborating with cross-functional teams. I often hold kick-off meetings to align on goals, and I use agile methodologies to ensure iterative feedback. Regular check-ins help in managing dependencies and maintaining momentum.

In a data-driven organization, a data engineer plays a vital role by creating and maintaining the infrastructure that ensures reliable data access for analysis. We translate raw data into structured datasets that empower data scientists and analysts to generate insights, fostering informed decision-making and organizational growth.

I stay current by following industry blogs, joining relevant communities, and participating in webinars. I also dedicate time for professional development through online courses and certifications. Additionally, I engage with peers and attend conferences to exchange ideas and insights about the latest technologies and methodologies.

I have a strong foundation in SQL and data analysis, with extensive experience in writing complex queries, optimizing performance, and conducting in-depth analyses for various business needs. My SQL skills allow me to extract actionable insights and transform data into formats that facilitate decision-making.