Role Description The Senior Data Engineer role at Spring Venture Group is an opportunity to work at the intersection of data pipelines, data modeling, analytics, AI/ML enablement, and modern cloud-native platforms, with Snowflake as your foundation. We are looking for someone who is excited to move away from building every pipeline from scratch and toward designing data products and AI-powered applications. As automation and platform capabilities advance, you will evolve from a traditional builder into a strategic architect of data-driven solutions. Essential Duties - Modern Data Platform & AI Enablement - Build advanced data pipelines utilizing the Medallion Architecture to create high-quality data sources in Snowflake. - Leverage AI-assisted development tools to reduce manual pipeline work and explore emerging Snowflake capabilities like Cortex to support AI/ML-enabled workflows. - Build Data Applications that contribute to interactive experiences, including Streamlit-based apps, transforming Snowflake from a warehouse into a full-service data platform. - Create robust data pipelines using AWS technologies to serve specific business needs. - Data Modeling & Business Impact - Design and evolve analytical data models that support BI, advanced analytics, and AI workloads. - Partner directly with Analytics and Data Science teams to translate complex business questions into well-structured, modular data assets. - Write complex Python/SQL scripts and stored procedures to ensure 99.95% uptime and optimize query performance for downstream consumption. - Leadership & Engineering Excellence - Serve as a primary advisor to the Data Management team to identify opportunities for technical improvements and automation. - Act as a leader for junior and mid-level engineers, providing direction on programming best practices, query optimization, and business tact. - Architect replacements for legacy systems with a focus on data governance and maintainable documentation, including flowcharts and clear code comments. - Support after hours and weekend releases from our internal Software Development teams. - Actively participate in code review and weekly technicals with another more senior engineer or manager. - Assist departments with time-critical SQL execution and debug database performance problems. Qualifications - Bachelor's degree in Computer Science, or a related technical field. - 4-5 years of practical production work in Data Engineering. - Expertise of the Python programming language. - Expertise of Snowflake. - Expertise of SQL, databases, & query optimization. - Must have experience in a large cloud provider such as AWS, Azure, GCP. - Advanced at reading code independently and understanding its intent. - Advanced at writing readable, modifiable code that solves business problems. - Ability to construct reliable and robust data pipelines to support both scheduled and event-based workflows. - Working directly with stakeholders to create solutions. - Mentoring junior and mid-level engineers on best practices in programming, query optimization, and business tact. Preferred Experience - Experience building or supporting Streamlit applications or similar data-driven apps. - Familiarity with Snowflake Openflow for data ingestion and orchestration. - Exposure to Snowflake Cortex or AI/ML-enabled analytics workflows. - Background in analytics engineering (dbt-style modeling, semantic layers, metrics design). Benefits - Competitive compensation. - Medical, Dental and Vision benefits after a short waiting period. - 401k matching program. - Generous paid time off program with an additional company break during the holidays. - Annual Volunteer Time Off (VTO) and a donation matching program. - Life Insurance. - Employee Assistance Program (health & well-being on and off the job). - Rewards and Recognition. - Maternity and Parental Leave. - Training program and ongoing support throughout your entire Spring Venture Group career. - Diverse, inclusive & welcoming culture. - Optional enrollment options include HSA/FSA, AD&D, Spousal/Dependent life insurance, Short Term/Long Term Disability, Travel Assist, and Legal plans.

Role Description Why you will enjoy Mondays again: - Opportunity to work alongside a talented, diverse team in a collaborative and creative environment - Ongoing investment in your growth through training, mentorship, and industry certifications - Hands-on exposure to cutting-edge technologies across multiple industries - Recognition programs and employee awards within an inclusive culture that celebrates great work, including our AI Innovators Program - Remote or hybrid work options - Competitive salary + bonus opportunities - Robust benefits package, matching 401(k) plan, and substantial PTO - Tuition reimbursement A Day in the Life: - Own data pipelines end to end—from ingestion and transformation through validation and delivery—while bridging modern cloud data architecture with legacy enterprise reporting systems still in active use. - Architect solutions for scale, including designing data models, partitioning and clustering strategies, and query optimization for datasets ranging from multiple terabytes to petabytes. - Apply advanced, hands-on Google Cloud Platform and BigQuery expertise to make architectural decisions related to partitioning, clustering, and cost and slot management. - Build and operate production pipelines from ingestion through final delivery, taking full ownership rather than contributing to only one part of a shared system. - Use advanced SQL to develop complex queries and optimize performance at scale. - Work extensively within legacy enterprise reporting environments using SQL Server, stored procedures, and SSRS. This is an active requirement, not simply a technology associated with a previous role. - Build data monitoring, reconciliation, and automated quality-assurance frameworks to support data validation and integrity. - Manage ETL/ELT processes and data integration at production scale. - Use intermediate-level Python for automation and data processing. - Apply strong database design principles across data solutions. - Work with data visualization tools such as Power BI, Looker, or comparable platforms; Tableau experience is transferable. - Approach work with strong analytical skills and attention to detail while independently managing responsibilities and delivering results. - Actively use AI tools, including Claude through our Anthropic partnership, to streamline tasks, improve the quality of your work, and share best practices with teammates while continuously identifying new ways to integrate AI into everyday workflows. Qualifications - Consultative approach and problem solving skills to successfully align digital solutions with long-term business goals of the client - Hands-on experience using AI tools to enhance daily work, or a strong desire to do so, including a willingness to experiment, learn, and champion AI adoption within your role - Commitment to understanding and exceeding client expectations - Ability to perform project oversight and execution of deliverables - Flexibility to adapt within a high-growth organization - Ability to lead, mentor and motivate those around them - Hunger for continuous learning and professional development - Intellectual curiosity to provide creative solutions - Full understanding of the software development life cycle - Ability to positively impact fellow colleagues through effective leadership, presentations, coaching, etc. - Desire to work in a team environment - Good interpersonal, written and verbal communication skills Equal Employment Opportunity Policy Sparq is proud to offer equal employment opportunity without regard to age, color, disability, gender, gender identity, genetic information, marital status, military status, national origin, race, religion, sexual orientation, veteran status, or any other legally protected characteristic. We are committed to providing equal employment opportunities and believe in an inclusive workplace. If you require reasonable accommodations to participate in the job application or interview process, please let us know by contacting recruiting@teamsparq.com . #LI-REMOTE

• Provide technical leadership to the Data Engineering team, promoting best practices and supporting team development; • Define and evolve the data platform architecture, ensuring scalability, performance, security, governance, and cost efficiency; • Design, review, and guide the implementation of data pipelines (ETL/ELT, batch and streaming), ensuring data quality, availability, and reliability; • Define development, architecture, data modeling, and system integration standards; • Develop and evolve solutions for ingesting and processing data from APIs, relational databases, legacy systems, and cloud services; • Evolve Data Lakes, Data Warehouses, and Data Marts to support Analytics, Artificial Intelligence, and business applications; • Implement observability, monitoring, and alerting strategies for pipelines, applications, and data infrastructure; • Ensure adoption of engineering best practices, including version control, automated testing, CI/CD, and Infrastructure as Code (IaC); • Conduct code reviews and design reviews, continuously raising the technical quality of deliverables; • Collaborate with Product, Software Engineering, and business teams to define and prioritize solutions; • Support strategic platform decisions, identifying opportunities for continuous improvement in architecture, performance, costs, and scalability.

• End-to-End Architecture & Delivery: Define, design, and implement complex data infrastructure, pipeline ingestion, and transformation layers spanning multiple business units (Esports, Festivals, Commerce, HR, Finance, and FACEIT); • Pipeline & Platform Engineering: Construct scalable architectures using well-architected framework principles (reliability, security, performance, and automation). Manage workflow orchestration, CI/CD pipelines, and infrastructure automation; • Data Modeling & Semantic Layers: Standardise enterprise-grade dimensional modeling (Kimball/Inmon), star schemas, and centralised semantic layers to define trusted, cross-pillar metrics; • AI-Ready Datasets: Create clean, optimised, AI-ready datasets with structured metadata, clear naming conventions, and explicit documentation to support downstream AI agents and cognitive models; • Technical Standards & Governance: Establish and enforce best practices for Python/SQL development, dbt optimisation, testing frameworks, and version control across the hybrid data domain; • Efficiency & Cost Optimisation: Proactively monitor and optimise performance and infrastructure platform costs across storage, compute, and querying layout (e.g., BigQuery configurations); • Stakeholder Partnership: Act as a strategic partner to business leaders and analysts, translating complex objectives into scalable self-service data products; • Incident Response & Data Integrity: Lead incident response efforts for both system downtime and data quality anomalies, implementing automated testing to ensure Industry Standard Data Integrity; • Mentorship & Growth: Mentor junior and mid-level engineers through rigorous code reviews and technical guidance, supporting their professional growth and advancing team standards.