Digital Remedy is looking for a skilled Data Engineer to join our growing team of analytics experts. As a Data Engineer, you will help design, build, maintain, and troubleshoot data processing systems, with an emphasis on reliability, scalability, and efficiency. You will also contribute to optimizing our company’s data architecture to support our next generation of products and initiatives.

MINIMUM REQUIREMENTS:

• 3+ years of hands-on experience in building and optimizing data solutions.
• Proficiency in Python, PySpark and SQL for data manipulation and analysis.
• Experience with data pipeline development (ETL/ELT) and workflow orchestration.
• Working knowledge of Spark (or another distributed processing framework).
• Familiarity with cloud platforms (e.g., Google Cloud Platform, AWS, or Azure).
• Understanding of data modeling, transformation, and performance optimization.
• Strong problem-solving and organizational skills.
• Ability to collaborate effectively in cross-functional teams.

PREFERRED QUALIFICATIONS:

• Hands-on experience with Databricks platform.
• Experience in Ad Tech, or Media industries.
• Prior involvement in mentoring junior engineers or leading smaller projects.
• Familiarity with ML workflows or integrating BI tools (e.g., Looker, Tableau
  Sinclair is proud to be an equal opportunity employer and a drug free workplace. Employment practices will not be influenced or affected by virtue of an applicant’s or employee’s race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), national origin, age, disability, genetic information, military or veteran status or any other characteristic protected by law.

Contribute to end-to-end data analytics solutions (from data ingestion through visualization) for large-scale, complex data ecosystems.

• Assist in designing and maintaining data pipelines and architectures in the cloud.
• Build and support analytics tools that provide actionable insights into customer acquisition, operational efficiency, and other key performance metrics.
• Identify opportunities for process improvements: automating manual processes, optimizing data delivery, and supporting scalable infrastructure.
• Model business processes for analysis and optimization.
• Take ownership of individual projects, communicate progress effectively, and showcase key accomplishments.
• Strive for continued learning opportunities to grow technical skills and business context.