• Architect & Lead Operational Data Flows by designing and overseeing the implementation of an Operational Data Store (ODS) • Build low-latency data streams using technologies like Kafka or Flink to power embedded analytics directly within customer-facing applications • Establish "Data Contracts" with upstream engineering teams to ensure high availability and schema stability for all real-time operational flows • Own the transition and scaling of our Analytical Data Store (e.g., Snowflake), ensuring it is optimized for both performance and cost-efficiency • Modernize transformation layer by implementing robust ELT patterns and modular data modeling (using dbt and airflow) • Champion Data Governance, ensuring that every dashboard and report is backed by high-quality, audited, and well-documented data • Build the "Data Foundation" for Machine Learning, including development of Feature Stores and automated pipelines for model training and inference • Mentor and grow a high-performing engineering team, fostering a culture of "DataOps" where automation, testing, and observability are the default • Act as a strategic partner to Product and Executive leadership, translating complex technical roadmaps into clear business value

• Work closely with business areas to understand needs and translate requirements into scalable data solutions aligned with the organization’s objectives. • Conduct requirements gathering and map business processes and data flows between systems. • Define and validate business rules, metrics, KPIs and corporate indicators. • Design and evolve data models for Data Warehouse, Data Lake and Lakehouse environments. • Define dimensional modeling strategies, including facts, dimensions, granularity and relationships. • Develop data architectures using Azure Data Factory, Databricks, Data Lake and Power BI. • Ensure data quality, consistency and traceability across data pipelines. • Define standards for data ingestion, transformation, quality and availability across Bronze, Silver and Gold layers. • Serve as a technical reference for Data Engineering, BI and Analytics teams. • Support solution implementation, ensuring adherence to architectural and business definitions. • Produce documentation for data models, business rules and corporate indicators.

• Inherit, evaluate, and take full ownership of existing ETL/ELT pipelines — identifying what to preserve, improve, or replace based on performance, reliability, and long-term maintainability. • Design and build scalable pipeline improvements or net-new solutions where current practices fall short. • Monitor pipeline health, troubleshoot data quality issues, and proactively resolve performance and reliability problems. • Manage and evolve orchestration tooling with openness to adopting better alternatives as infrastructure needs grow. • Optimize query performance, pipeline efficiency, and resource utilization across Convo’s data environment. • Participate in testing, deployment, and monitoring practices that promote long-term reliability and scalability. • Develop and maintain scalable data transformation processes, schema design, and data models that support evolving business requirements. • Establish and evolve data quality testing frameworks - building practices that catch issues early and create lasting internal trust in our data. • Own data governance, documentation, lineage, version control, and data quality standards across the organization. • Serve as the primary internal resource for data engineering guidance and recommendations, helping set standards and informing data infrastructure decisions across the organization. • Work closely with the data analyst to translate business questions into reliable, queryable data structures. • Educate and guide non-technical stakeholders on how to work effectively with data, what is and isn’t feasible, and how to frame data requests clearly. • Explore and implement tooling to enable self-service data discovery for internal teams, reducing bottlenecks and empowering stakeholders to answer their own questions. • Collaborate with Product, Engineering, Finance, Operations, and Data Science stakeholders to support reporting, forecasting, and business intelligence needs. • Partner with Product and Engineering teams to integrate analytics, event tracking, and reporting into products and platforms. • Establish and document data engineering standards, workflows, and best practices at Convo — building a foundation that is sustainable, well-understood, and not dependent on any single person. • Contribute to improvements in data architecture, tooling, monitoring, automation, and engineering best practices. • Evaluate emerging technologies and tooling to improve efficiency, automation, and accessibility of data systems. • Maintain clear technical documentation and operational standards that support long-term maintainability. • Exercise sound technical judgment in balancing immediate business needs with long-term platform sustainability. • Maintain strong confidentiality and discretion when handling sensitive organizational, financial, operational, and employee data.

• Architect, build, and maintain robust data pipelines • Design and implement analytical data models • Partner with software engineering teams for data ingestion pipelines • Implement validation frameworks and quality checks • Contribute to the evolution of the data platform architecture