• 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

• Apply engineering principles and methodologies to solve complex technical challenges • Troubleshoot technical issues and develop effective solutions in a timely manner • Stay up to date with the latest advancements in relevant technologies • Collect, analyze, and interpret large datasets using Excel and other analytical tools • Develop and maintain insightful reports and dashboards to track key performance indicators (KPIs) and identify trends • Use data-driven insights to make recommendations for process improvements, cost reduction, and efficiency gains • Communicate technical information clearly and effectively to both technical and non-technical audiences • Collaborate with cross-functional teams (e.g., sales, marketing, operations) to achieve common goals • Present technical findings and recommendations to management in a concise and persuasive manner • Act as a technical resource and mentor to junior engineers • Understand the business implications of engineering decisions and contribute to strategic planning • Identify opportunities to leverage technology to improve business processes and gain a competitive advantage • Participate in cost-benefit analyses and ROI calculations for engineering projects • Stay informed about industry trends and market dynamics • Develop project plans, track progress, and identify and mitigate risks