• Enable self-service analytics for all team members by designing clean, intuitive data models and metrics through dbt, empowering employees to make informed, data-driven decisions. • Develop and refine custom data pipelines that ingest data from operational systems to our analytics platform, handling both streaming and batch data using third-party tooling and homegrown solutions. • Maintain and optimize the data platform infrastructure, focusing on data quality, ELT efficiency, and platform hygiene. • Architect and implement key components of the analytics infrastructure, such as BI, semantic layers, and a foundational data warehouse. • Develop and maintain streaming data pipelines from various databases and data sources. • Collaborate across business units to understand data needs and ensure the required data is collected, modeled, and available to team members. • Document best practices and coach/advise other data analysts, product managers, engineers, etc. on data modeling, SQL query optimization & reusability, etc. Keep our data platform tidy by managing roles and permissions and deprecating old projects.

Role Description We are seeking a data-driven BI Engineer to join our team at a high-growth advertising technology company. This role focuses on scaling our reporting infrastructure for advertising performance and billing reconciliation, ensuring that financial and operational data is accurate, automated, and actionable. - Develop robust data pipelines, ensuring data quality and reliability. - Enable efficient data consumption across the organization. - Collaborate closely with cross-functional teams including Product, Engineering, Analytics, and Business stakeholders to deliver high-impact data platforms. The ideal candidate is a proactive problem-solver with strong technical expertise, capable of working with large datasets, modern data architectures, and cloud-based environments. You thrive in fast-paced settings, navigate ambiguity with confidence, and are passionate about turning data into actionable value. Qualifications - Strong experience working with Databricks Lakehouse architecture. - Experience designing and maintaining scalable data pipelines. - Ability to process and model large-scale analytical datasets. - Expert-level SQL development, including: - Window functions - Performance optimization - Complex joins and aggregations - Experience using Jinja SQL templating (dbt or similar frameworks). - Ability to build modular and reusable SQL transformations. Requirements - Exposure to AI-powered data workflows, particularly where AI assists in data processing, enrichment, or anomaly detection. - Integrating LLM APIs (e.g., OpenAI, Anthropic) into data pipelines. - Using AI for: - Automated data classification - Anomaly detection - Data enrichment - Exposure to Model Context Protocol (MCP) or similar approaches to connect AI models with external tools and data sources. Key Responsibilities - Build, maintain, and optimize scalable data pipelines and workspaces within the Databricks environment. - Develop and audit complex reporting models focused on advertising performance metrics and financial billing. - Write high-performance SQL queries and leverage Jinja SQL to create dynamic and reusable transformations. - Design data models that support accurate and actionable business insights. - Collaborate with cross-functional teams to ensure data reliability and reporting accuracy. - Explore and implement basic AI/ML enhancements such as predictive billing models or anomaly detection. - Support automation efforts through CI/CD practices using GitHub and Jenkins. - Maintain clean and well-documented configuration files using YAML/YML. - Continuously improve data workflows for efficiency, scalability, and quality. Benefits - Monetary compensation - Year-end Bonus - IMSS, AFORE, INFONAVIT - Major Medical Expenses Insurance - Minor Medical Expenses Insurance - Life Insurance - Funeral Expenses Insurance - Preferential rates for car insurance - TDU Membership - Holidays and Vacations - Life happens days - Bereavement days - Civil Marriage days - Maternity & Paternity leave - English and Spanish classes - Performance Management Framework - Certifications - TALISIS Agreement: Discounts at ADVENIO, Harmon Hall, U-ERRE, UNID - Taquitos Rewards - Amazon Gift Card on your Birthday - Work-from-home Bonus - Laptop Policy Equal Employment Enroute is committed to providing equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

Role Description Build the data foundations of our monetisation platform. Thanks is building a customer-first monetisation platform that delivers growth without compromise – for advertisers, publishers, and customers. We’re hiring a Staff Data Engineer to build our data foundations from the ground up. This is our first dedicated data engineering hire; a senior individual contributor who will design and deliver the data architecture, models and products that will scale the success of Thanks. This role is deeply hands-on, highly influential, and foundational to the future of our engineering and product organisation. - Build the data platform: Design and deliver a scalable platform that serves as the primary engine for the Thanks Network. - Own the models: Responsible for the data science, technical implementation, and performance of our ranking systems. - Build for real-time inference: Own the end-to-end lifecycle of our models – from training and validation to real-time inference. - Unlock model experimentation: Build the framework that allows us to run experiments on our ranking systems. - Own pipelines & observability: Build robust batch and near-real-time pipelines that are resilient and observable. - Enable self–serve analytics: Design clean, trusted datasets and data marts for product, engineering, and commercial teams. - Set the data direction: Define tooling, architecture, and trade-offs as our data needs evolve. - Lead through expertise: Act as the go-to expert for data across the business. Qualifications - Experience operating as a senior, hands-on individual contributor in high-growth environments. - Deep strength in both data engineering and applied data science. - Experience building and operating data pipelines in cloud environments. - Hands-on experience with analytical databases. - Familiarity with streaming or event-driven data architectures. - Comfortable operating as a senior IC in a greenfield environment. - Excellent communication skills and the ability to partner effectively across teams. - Uses AI thoughtfully to augment exploration, modelling, and engineering workflows. - Strong internal drive – cares deeply about performance, correctness, and building systems that last. Requirements - Data Engineering: PySpark, dbt, strong SQL skills (must have). - Data Workflow Pipeline: one of Airflow, Dagster, Step Functions or equivalent (must have, at least 1). - DevOps / DataOps: Terraform, CloudFormation, Azure ARM, Kubernetes (must have, at least 1). - Data Warehouse: Databricks, Snowflake, BigQuery, ClickHouse, Redshift, etc. (must have, at least 1). - Data Catalog / Feature Store: Databricks Unity Catalog, Atlas (nice to have). - Event Streaming: Kafka, Kinesis, or equivalent (nice to have). - Data Analytics / Reporting: Experience working on or supporting reporting functions such as Tableau, Power BI, Superset, etc. (nice to have). - Data QA: Great Expectations, DBT testing, etc (nice to have). Benefits - Foundational ownership: Build and own core data foundations from the ground up. - Impact you can see: Your work directly influences product performance and experimentation. - Strategy meets execution: This is a hands-on role while setting direction for a fast-growing platform. - Growth, without chaos: Work closely with founders and Head of Product in a supportive culture. - Attractive compensation: Including meaningful equity. - Flexibility with intent: Open to exceptional candidates across Australia’s east coast.

• Partner with Sales, Marketing, Operations, and CSM leaders to define and operationalize Growth & Retention KPIs (acquisition, activation, expansion, churn, LTV, NRR). • Build and maintain a B2B Customer 360 data model integrating product usage, contracts, billing, support, engagement, and operational signals. • Design, build, and maintain analytics data models and semantic layers that power dashboards and AI-driven insights. • Build executive and operational dashboards focused on growth performance and customer health. • Develop churn prediction models, customer health scores, and expansion propensity models. • Identify leading indicators for churn, renewal risk, and upsell opportunities. • Leverage AI/ML and LLM-based techniques for: Automated insight generation, Anomaly detection, Opportunity and risk scoring, Natural language analytics (e.g., text-to-SQL, insight summaries) • Ensure high data quality, reliability, and observability in collaboration with data engineering. • Drive best practices in analytics engineering (testing, version control, documentation, code reviews). • Influence analytics strategy and roadmap for Growth & Retention and Customer 360.