• Work across infrastructure, backend services, CLI tooling, and dashboard interfaces to deliver cohesive platform capabilities. • Drive architectural decisions that balance scalability, security, and developer experience. • Build APIs and internal systems that abstract complex infrastructure into simple, developer-friendly workflows. • Implement and maintain edge compute or middleware capabilities for request handling, authentication, and response transformation. • Architect reliable, scalable, and performant compute infrastructure to support high levels of parallel builds and deployments. • Implement observability across the build pipeline (metrics, tracing, logging) to diagnose bottlenecks and improve system resilience.

• Design and build intelligent automated templates • Turn flat Word precedents into structured, automated templates using Avvoka’s platform. • Apply coding fundamentals, conditional logic, and variable fields to create reliable and dynamic documents. • Translate structured instructions and client briefs into functional, scalable automation solutions. • Test, debug, and optimise your creations • Conduct thorough quality checks to ensure templates generate accurately under multiple scenarios. • Troubleshoot and resolve logic, formatting, or automation errors. • Maintain reusable, scalable structures that can evolve with client needs. • Work directly with clients to understand their document automation requirements. • Partner with Customer Success and Product teams to ensure templates meet best practices. • Advise on contract structure to improve automation efficiency and clarity. • Update and improve templates based on feedback and evolving client requirements. • Implement dynamic fields, integrations, and conditional workflows to support complex contracts. • Ensure consistency and alignment across templates with client branding and formatting standards.

Data Product Engineer Job Description We are seeking an experienced and highly motivated Data Product Engineer to bridge the gap between engineering and product management. This role involves owning the end-to-end lifecycle of our data products, from strategic conception and requirement gathering to technical implementation, quality assurance, and adoption. The ideal candidate will leverage a strong background in scalable data engineering, cloud architecture, and business intelligence to transform complex business problems into reliable, high-value data assets and actionable insights.Key Responsibilities. ●      Product Strategy & Roadmap: Partner with Product Managers, Business Analysts, and key stakeholders (Finance, Operations) to define the vision, strategy, and roadmap for critical data products. ●      Requirements & Design: Translate high-level business objectives and user needs into detailed, clear, and actionable data requirements, architecture, and technical designs for data products. ●      Data Pipeline Development: Design, build, and maintain highly scalable and reliable ETL/ELT data pipelines using modern cloud tools (e.g., Azure Data Factory, AWS Glue, Databricks, PySpark) to integrate diverse data sources. ●      Data Modeling & Warehousing: Develop and optimize performant data models (Star/Snowflake schema) in cloud data warehouses (e.g., Snowflake, BigQuery, Synapse) with a focus on data quality, governance, and compliance. ●      Quality & Governance: Implement data quality checks, data lineage tracking (e.g., dbt), and robust testing to ensure the accuracy and trustworthiness of all delivered data products. ●      Visualization & Adoption: Lead the delivery of BI solutions (e.g., Power BI, Tableau), ensuring dashboards and reports are intuitive, accurate, and drive measurable business decisions. ●      Deployment & Operations: Utilize Agile methodologies and CI/CD practices (e.g., GitHub, Azure DevOps, Jenkins) to deploy, monitor, and optimize data products, ensuring high availability and cost efficiency. ●      Mentorship & Collaboration: Act as a technical leader, mentoring junior team members and fostering a culture of data-as-a-product within the organization.

Data Product Engineer Job Description We are seeking an experienced and highly motivated Data Product Engineer to bridge the gap between engineering and product management. This role involves owning the end-to-end lifecycle of our data products, from strategic conception and requirement gathering to technical implementation, quality assurance, and adoption. The ideal candidate will leverage a strong background in scalable data engineering, cloud architecture, and business intelligence to transform complex business problems into reliable, high-value data assets and actionable insights.Key Responsibilities. ●      Product Strategy & Roadmap: Partner with Product Managers, Business Analysts, and key stakeholders (Finance, Operations) to define the vision, strategy, and roadmap for critical data products. ●      Requirements & Design: Translate high-level business objectives and user needs into detailed, clear, and actionable data requirements, architecture, and technical designs for data products. ●      Data Pipeline Development: Design, build, and maintain highly scalable and reliable ETL/ELT data pipelines using modern cloud tools (e.g., Azure Data Factory, AWS Glue, Databricks, PySpark) to integrate diverse data sources. ●      Data Modeling & Warehousing: Develop and optimize performant data models (Star/Snowflake schema) in cloud data warehouses (e.g., Snowflake, BigQuery, Synapse) with a focus on data quality, governance, and compliance. ●      Quality & Governance: Implement data quality checks, data lineage tracking (e.g., dbt), and robust testing to ensure the accuracy and trustworthiness of all delivered data products. ●      Visualization & Adoption: Lead the delivery of BI solutions (e.g., Power BI, Tableau), ensuring dashboards and reports are intuitive, accurate, and drive measurable business decisions. ●      Deployment & Operations: Utilize Agile methodologies and CI/CD practices (e.g., GitHub, Azure DevOps, Jenkins) to deploy, monitor, and optimize data products, ensuring high availability and cost efficiency. ●      Mentorship & Collaboration: Act as a technical leader, mentoring junior team members and fostering a culture of data-as-a-product within the organization.