• Understand Cogstate data sources and develop data pipelines using Databricks to bring all data into the data lake. • Design, develop, implement, and tune large-scale distributed systems and pipelines that process large volumes of data; focusing on scalability, low-latency, and fault-tolerance in every system. • Developing scalable and re-usable frameworks for ingesting data into Azure Databricks, incorporating standards and best practices into engineering solutions. • Databricks engineering - query tuning, performance tuning, troubleshooting, and debugging pipelines. • Deep understanding of ETL/ELT design methodologies, architecture, strategy, and tactics for complex ETL solutions, including CI/CD skills. • Develop high performance scripts in PySpark to achieve objectives of enterprise data, BI, data visualization and analytics needs. • Data processing/transformation using various technologies such as Apache Spark, SQL, Python/Scala and Azure cloud services. • Manage code versions in source control and coordinate changes across teams by leveraging Github. • Participate in architecture design and discussions, provide logical and physical data design, and database modelling. • Be part of the Agile team to ensure availability of data to internal and external users. • Organize and manage data shares. • Solve complex data issues around data integration, data quality, and other data processing incidents. • Work with business system owners to resolve source data issues and refine transformation rules.

GSTV is dedicated to building an inclusive team and culture that reflects the communities we serve every day. Being part of the GSTV team means that we are always encouraged and challenged to grow personally and professionally. More importantly, we are accountable for our actions towards one another as the foundation for a strong and accepting workplace. GSTV Values: ●      Growth Focused ●      Social Accountability ●      Tenacious Behavior ●      Valued Actions Benefits Day One! Medical, Dental, Vision, Paternal Leave, Life Insurance, Accident, Critical Illness, Hospital Indemnity, STD/LTD + Vol Plans., Paid Holidays, 20 PTO days + Sick time, Perks, HSA and FSA and Lifestyle Spending Account (1st of mo after 30 days), 401K Match (90 days). GSTV offers both hybrid and remote work situations. Candidates located within commutable distance to our New York Office will be tagged to that office and are not currently considered 100% remote. SENIOR DATA ENGINEER Summary: At GSTV, we take pride in the data that powers our media platform and are committed to unlocking its full potential to drive meaningful business outcomes. That’s why we’re expanding our Data Engineering team and looking for a talented Senior Data Engineer to join us. As a Senior Data Engineer, you'll play a key role in building and maintaining the pipelines and platforms that enable our teams to access trusted, timely, and actionable data. You’ll work closely with Software Engineering, Architecture, IT, and Analytics to deliver scalable, high-quality data solutions that support our advertising, hardware, and retail partners. Your primary focus will be on building efficient and reliable data pipelines, ensuring data quality, supporting analytics and reporting, and helping evolve GSTV’s data platform to meet future business needs. Along the way, you’ll mentor other engineers, help establish best practices, and collaborate across the organization to deliver measurable impact. We’re passionate about being a data-driven organization, and we need someone who shares that passion. If you're excited to solve complex challenges, thrive in a fast-paced environment, and want to shape the future of data at GSTV, we’d love to hear from you. Your success will be measured by: Your ability to build and maintain robust, scalable data pipelines that meet business needs. The reliability and accuracy of the data systems you deliver—ensuring high data quality and visibility of any system issues. Your contributions to the technical design and implementation of data infrastructure and pipelines. Your ability to mentor and coach other members of the engineering team. Your ability to collaborate effectively with engineering, architecture, product, and analytics groups. Your ability to stay current with modern data engineering trends and apply them effectively at GSTV. Technologies We Use: Data & Visualization: Snowflake, Domo, MongoDB, Dynamo Cloud & Infrastructure: AWS (Kinesis, Lambda, S3, SQS, CDK) Languages: Python, SQL Integrations: SaaS APIs (e.g., Salesforce) Development Practices: Agile, Git, CI/CD Responsibilities: Responsibilities include, but are not limited to: Deliver high-quality data solutions on time and within scope, while meeting the highest quality standards. Design, develop, and maintain secure, scalable ETL pipelines that process structured and unstructured data from various sources. Build and optimize data models and processing workflows to support business intelligence, analytics, and reporting needs. Develop monitoring and alerting solutions to ensure pipeline performance and data quality, with quick visibility into issues. Collaborate closely with software engineering, architecture, product management, analytics, and IT teams. Translate business requirements into robust technical solutions and provide input on feasibility, scalability, and design. Requirements: You are Detail-oriented with the ability to drill down into tactical considerations. Able to act autonomously while following team philosophy and guidelines. Resourceful—able to find solutions even when they are not obvious—and know when to ask for help. Comfortable with ambiguity and rapid change. A team player with strong communication and collaboration skills in a matrixed environment. Business and outcome-focused with a bias toward action. Skilled at identifying and resolving data quality issues. Passionate about building well-structured, maintainable, and scalable data systems. A self-starter and lifelong learner, always eager to experiment with new tools and technologies. You have, ideally, Strong proficiency in SQL and data modeling techniques. Deep experience with Snowflake or comparable data platforms. Hands-on experience with Python and familiarity with other languages. Expertise in integrating data from external APIs and third-party systems. Familiarity with infrastructure-as-code and cloud-native data services (especially AWS). Strong analytical thinking, problem-solving, and debugging skills. Effective and impactful oral, written, and presentation communication skills. Experience in a fast-paced, Agile environment. Strong influencing skills—you can achieve goals without direct authority. Excellent organization and time management skills. The requirements listed are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. Education and/or Experience: Experience: Ideal candidates will have 5+ years of experience developing and maintaining ELT systems, and a high-level understanding of different data processing systems. Must Have Experience with the data warehouses (Snowflake or BigQuery), data ELT/ETL pipelines Proficiency in data modeling, advanced SQL, stored procedures, and query optimization Proficiency in one or more programming languages such as Python, Java, or Scala Nice To Have Creating datasets, dataflows, and dashboards in Domo Experience building integrations with SaaS APIs like Salesforce Education: Bachelor’s degree in Computer Science or a related field Extra considerations given for certifications such as AWS Data Analytics Certification or Snowflake Data Engineering Certification Of course, this is just a sample of the kinds of work this role will require! You should assume that your role will encompass other tasks, too, and that your job duties and responsibilities may change from time to time at GSTV's discretion, or otherwise applicable with local law. Note: The expected salary range for this position is based on a combination of experience and qualifications for the position, as well as geographic location to align with local market.

• Be a data champion and seek to empower others to leverage the data to its full potential • Understand our complex data ecosystem • Work with the product team and stakeholders to translate business requirements for data across the company into a technical roadmap and architecture for the platform • Act as the leading data domain expert and owns platform data architecture • Lead the technical design and implementation of reliable, scalable, and efficient data infrastructure, data-driven products, and software solutions for external and internal customers • Provide technical leadership to define overall data engineering best practices, standards, and architectural approaches and drive technical excellence. Identify design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc. • Create and maintain optimal data pipeline architecture with high observability and robust operational characteristics • Assemble large, complex data sets that meet functional / non-functional business requirements • Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL • Work with stakeholders, including the Executive, Product, Clinical, Data, and Design teams, to assist with data-related technical issues and support their data infrastructure needs. • Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader

• Design and implement scalable, cloud-native data pipelines for both batch and streaming use cases using tools such as Azure Data Factory, Databricks, Spark, and Kafka. • Build and maintain multi-dimensional data models optimized for performance, cost, and maintainability. • Integrate upstream data sources (ERP, CRM, external APIs) into core systems with strong attention to data integrity and lineage. • Optimize data flows for storage, processing, and performance using best practices within the Azure ecosystem. • Participate in a shared on-call rotation with Senior and Principal Engineers to ensure data platform reliability. • Resolve production issues, perform root cause analysis, and communicate effectively with stakeholders. • Support operational data tasks (e.g., schema changes, table updates) to maintain agility and consistency. • Contribute to process improvement and operational metrics that enhance system performance and team efficiency. • Partner with product managers, analysts, and engineers to deliver well-architected, scalable data solutions. • Provide peer mentorship and participate in code and design reviews to elevate technical standards. • Influence tooling, architecture, and design decisions that shape the long-term direction of the data platform. • Contribute to documentation, reusability practices, and knowledge sharing across teams.