Software Engineer II, Big Data, tvScientific

• Design and implement robust data infrastructure in AWS, using Spark with Scala
• Evolve our core data pipelines to efficiently scale for our massive growth
• Store data in optimal engines and formats, matching your designs to our performance needs and cost factors
• Collaborate with our cross-functional teams to design data solutions that meet business needs
• Design and implement knowledge graphs, exposing their functionality both via Batch Processing and APIs
• Leverage and optimize AWS resources while designing for scale
• Collaborate closely with our Data Science and Product teams
• How we'll define success:
• Successful design and implementation of scalable and efficient data infrastructure
• Timely delivery and optimization of data assets and APIs
• High attention to detail in implementation of automated data quality checks
• Effective collaboration with cross-functional teams

What we're looking for:

• Production data engineering experience
• Proficiency in Spark and Scala, with proven experience building data infrastructure in Spark using Scala is preferred
• 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
• Strong proficiency in AWS services
• Expertise 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: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables
• Nice-to-haves:
• Experience in adtech
• Experience implementing data governance practices, including data quality, metadata management, and access controls
• Strong understanding of privacy-by-design principles and handling of sensitive or regulated data
• 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