Software Engineer II, Big Data, tvScientific

About the role

MtvScientific is seeking a Data Engineer to join our dynamic team. You will play a crucial role in developing and maintaining our robust data infrastructure, ensuring scalability and efficiency as we grow.

Responsibilities

• Design and implement robust data infrastructure in AWS, using Spark with Scala
• Evolve core data pipelines to efficiently scale for massive growth
• Store data in optimal engines and formats, aligning with performance needs and cost factors
• Collaborate with cross-functional teams to design data solutions meeting business needs
• Design and implement knowledge graphs, exposing functionality via batch processing and APIs
• Leverage and optimize AWS resources while designing for scale
• Collaborate closely with Data Science and Product teams

Requirements

• Production data engineering experience
• Proficiency in Spark and Scala, with experience building data infrastructure in Spark using Scala
• Experience in delivering significant technical initiatives and building reliable, large-scale services
• Experience in delivering APIs backed by relationship-heavy datasets
• Familiarity with data lakes, cloud warehouses, and storage formats
• Expertise in AWS services
• Strong proficiency in SQL for data manipulation and extraction
• Excellent written and verbal communication skills
• Bachelor's degree in Computer Science or a related field
• Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs
• Strong track record of critical evaluation and verification of AI-assisted work (e.g., testing, source-checking, data validation, peer review)
• High integrity and ownership: protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables

Qualifications

• Nice-to-haves: Experience in adtech, implementing data governance practices, familiarity with data table formats like Apache Iceberg, Delta, previous experience building out a Data Engineering function, proven experience working closely with Data Science teams on machine learning pipelines