• Design, develop, and maintain Power BI datasets, semantic models, and reports for global claims reporting • Implement best-practice data modelling (star schema, claim-centric fact tables, conformed dimensions) • Develop complex DAX measures for claims KPIs (e.g. incurred, paid, reserves, ultimates, movements) • Optimise report performance (model size, DAX efficiency, incremental refresh) • Ensure consistency of KPIs and definitions through a centralized data model • Work with Azure Databricks (Delta Lake) as the primary reporting data source • Contribute to the design and implementation of curated data layers (e.g. silver/gold) • Develop and maintain SQL / Spark-based transformations aligned with reporting requirements • Support data quality, reconciliation, and traceability from source systems to reporting outputs • Collaborate with data product teams and follow platform standards and governance • Translate business reporting requirements into scalable technical solutions • Contribute to agile delivery (backlog refinement, sprint execution, documentation) • Promote knowledge sharing and best practices within the team

The Data Engineer is responsible for designing, building, and optimizing data solutions in cloud environments, with a strong focus on Azure and participation in cross-cloud migration initiatives. This role supports data architecture decisions, migration processes, and performance optimization to ensure scalable and reliable data platforms. Responsibilities - Collaborate on cloud data migration initiatives, particularly from Azure to other environments - Design scalable and efficient data structures - Define storage solutions based on performance, cost, and scalability - Build and maintain ETL/ELT data pipelines - Design and execute cross-cloud data migration processes - Support co-hosting and gradual data transition strategies - Optimize cloud data architectures and workflows - Work with cross-functional engineering teams - Perform testing and validation of migration processes - Document technical solutions and data flows - Support reporting and analytics data requirements Requirements - Experience as a Data Engineer or similar role - Strong experience working with Microsoft Azure - Experience in cloud data migration projects - Knowledge of relational and NoSQL databases - Experience designing and maintaining ETL/ELT pipelines - Understanding of cloud data architecture - Strong problem-solving skills - Experience working in collaborative environments Nice to Have - Experience with AWS environments - Knowledge of Power BI - Experience with Microsoft Fabric - Experience with data integration tools - Knowledge of data warehousing concepts - Experience with data modeling Soft Skills - Problem-solving mindset - Collaborative work style - Cross-functional teamwork - Analytical thinking - Communication skills - Adaptability - Proactivity * By applying to this position, we’ll create your Simera Professional Key (SPK) — a unique key that helps you connect with employers, stand out, and secure the right match.

Drive independent projects in cybersecurity, develop executive dashboards using Looker Core and BigQuery, implement alerting systems for anomaly detection, and design data pipelines leveraging Google Cloud technologies for analytics and reporting.

Role Description This role exists to build the technical backbone that finally unifies our data. The Data Engineer will establish the structures, standards, and pipelines that turn scattered information into a trusted, governed, and scalable ecosystem. By creating proper schemas, mapping logic, and automated ETL processes, this role ensures that every team, from Operations to Finance to Analytics, can work with accurate, dependable data. We’re looking for a builder: someone technical, meticulous, and systems‑driven who can architect a long‑term foundation for how data flows, transforms, and supports our business decisions. What You’ll Do - Data Architecture & Schema Design - Evaluate and restructure existing data architecture to support scalability and performance. - Design new schemas, relationships, and data models that align with business logic and analytics needs. - Build and maintain a HelpGrid-centric data layer that consolidates fragmented sources into a central structure. - Provide strategic guidance on how data should be organized, named, and modeled for long-term sustainability. - Establish best practices for schema versioning, documentation, and change control. - ETL / ELT Development - Design and implement the company’s first ETL framework, defining how data is extracted, transformed, and loaded from multiple sources. - Build automated, reliable pipelines that move data from the centralized database and external tools (ClickCRM, Twilio, LeadLoop, Finance, etc.) into analytics-ready structures. - Standardize transformation logic to clean, normalize, and enrich data for business use. - Implement pipeline monitoring, error handling, and validation for data quality assurance. - Workflow Optimization & Advisory - Provide architectural and workflow recommendations for how data should flow between systems and teams. - Define how analysts should access, refresh, and use data safely and consistently. - Partner with the Data & Analytics Manager to align the engineering roadmap with BI and reporting priorities. - Develop scalable, reusable scripts and frameworks that simplify ongoing data management. - Integration & Performance Optimization - Integrate data from internal and third-party platforms into a centralized environment. - Optimize query and pipeline performance for high-volume operations. - Build APIs or microservices for data synchronization and access. - Collaborate with DevOps to ensure infrastructure stability, backup policies, and monitoring. - Governance, Security, & Documentation - Document data lineage, schema definitions, and system dependencies. - Implement data access controls, validation checks, and compliance standards. - Maintain transparent documentation for analysts, developers, and leadership. - Promote data stewardship and governance best practices across departments. Qualifications - 3–5 years of experience in data engineering, architecture, or backend development. - Proven success building or restructuring data environments for scalability and reliability. - Strong command of SQL and at least one programming language (Python, NodeJS, or similar). - Experience designing and implementing ETL/ELT frameworks from scratch. - Hands-on experience with data warehouses or data lakes (Azure, Microsoft Fabric, Snowflake, or AWS Redshift). - Solid understanding of data modeling principles, including star schema and normalization. - Familiarity with APIs, data validation, and version control (Git). Preferred Skills - Experience with Clickhouse, Redis, or Cassandra. - Familiarity with BI tools like Power BI, Looker, or Tableau. - Experience with orchestration tools (Airflow, Docker, Kubernetes). - Strong documentation habits and systems-thinking skills. - Ability to translate technical concepts into clear recommendations. Location Remote Department Data & Analytics Reports To CEO Note During onboarding and probation, this role will work under the guidance of the current Data / Product Manager (interim supervisor). Employment Type Full-Time