Data Engineer

• Build new reports for module releases, working with PMs on the brief and engineering on the source data
• Refactor existing reports to a common standard for layout, performance, and UX
• Tune slow queries and dashboards; fix bottlenecks at the SQL, schema, or aggregation layer
• Maintain the modeling / semantic layer in Metabase so non-technical users can self-serve where appropriate
• Define and apply naming, formatting, and visual standards for reports across the platform
• Partner with engineering on schema and event design so the data needed for reporting exists at the source
• Build reusable report templates that help our implementation team deliver faster on customer-specific requests
• Track usage and feedback on reports; double down on what matters, retire what doesn't
• Support AI and ML initiatives by preparing and maintaining high-quality datasets the AI features on our platform can build on

BI and Data Engineer

• Support the development and optimisation of neural‑network‑based models for on‑board processing.
• Contribute to dataset preparation, preprocessing, and evaluation workflows.
• Assist in prototyping machine‑learning algorithms in Python (PyTorch, TensorFlow, etc.).
• Participate in model optimisation for embedded or resource‑constrained hardware.
• Help integrate,test and benchmark AI algorithms on experimental on‑board computing platforms/accelerators (such as Neuromorphic or FPGA-based).
• Document technical results and support internal reviews.
• Work under the guidance of senior engineers in the Orbital-HPC team.

AI/ML Data Engineer

• - Design, build, and maintain production-grade ELT pipelines from our PostgreSQL production DB and third-party sources into Snowflake, ensuring reliable, timely data delivery to support all downstream analytics and reporting
• - Lead the design and implementation of a Medallion Architecture (Bronze/Silver/Gold) in Snowflake, establishing clear layer definitions, ingestion standards, and handoff protocols with the Analytics Engineer
• - Lead the migration from dbt Core to dbt Cloud, including project restructuring, scheduling setup, CI/CD pipeline configuration, and environment management
• - Implement data quality checks, monitoring, and alerting at the ingestion layer — ensuring pipeline stability and proactive issue detection before downstream impact
• - Define and enforce data contracts with upstream Engineering teams; manage Snowflake environment including roles, permissions, warehouse sizing, and cost optimisation
• - Design and implement reverse ETL flows to push enriched data back into third-party tools (e.g. HubSpot); document pipelines and ingestion logic to reduce key-person risk

Senior Data Engineer

• Design, develop, and optimize scalable data pipelines and ETL/ELT processes.
• Define and implement enterprise-wide data quality principles, frameworks, and standards.
• Ensure data pipelines deliver reliable, accurate, and high-quality data across platforms and business domains.
• Design and implement strategies that make data Findable, Accessible, Interoperable, and Reusable (FAIR).
• Build and maintain scalable datasets and data models that support analytics and AI/ML initiatives.
• Collaborate closely with AI, Data Science, Analytics, and Engineering teams to support AI-related projects and production workloads.
• Ensure data assets are cataloged, and metadata (business and technical) is properly maintained to improve discoverability and trust.
• Work with engineers, analysts, and business stakeholders to define data quality requirements for dashboards, models, and operational processes.
• Drive best practices across data architecture, governance, testing, monitoring, documentation, and CI/CD processes.
• Support cloud-native and multi-cloud data solutions across different cloud providers.
• Improve observability, reliability, security, and operational excellence across the data platform.

Senior Data Engineer – Data Quality

• Be part of the team that will migrate on-premises data systems to cloud solutions, ensuring data integrity and minimal downtime
• Design, develop, and optimize data pipelines for Bronze, Silver, and Gold layers within the Microsoft Fabric ecosystem
• Monitor and troubleshoot cloud infrastructure performance, ensuring scalability and efficiency
• Collaborate with the team to implement best practices for data governance, security, and compliance in the cloud
• Document workflows, architecture, and key processes to support team operations and future scalability
• Stay abreast of the latest data engineering technologies and trends, particularly within the Microsoft Azure ecosystem, and identify opportunities to leverage new tools and techniques
• Manage and modernize existing on-premise data solutions (e.g., SQL Server, SSIS, SSAS) while supporting migration initiatives to cloud platforms
• Optimize data storage, retrieval, and transformation processes across hybrid environments
• Ensure data availability, consistency, and synchronization between on-premise systems and cloud-based services
• Support and enhance hybrid data workflows and design patterns (e.g., staged migration, dual-write strategies)

Junior Data Engineer

• - Design, develop, and maintain data solutions using SQL Server and SSIS
• - Build and optimize ETL processes, stored procedures, queries, views, and database objects
• - Ensure smooth data integration between internal systems, external platforms, and applications
• - Support and improve database performance, reliability, and data quality
• - Write, maintain, and enhance .NET / C# code to support data-related applications, services, and integrations
• - Troubleshoot data issues, system integrations, and performance bottlenecks
• - Translate business requirements into efficient technical solutions
• - Maintain documentation for data processes, integrations, and database changes
• - Contribute to technical improvements, best practices, and code reviews

• Participate in full software development lifecycle implementing, testing and maintaining custom data solutions.
• Select appropriate methods for collecting and analyzing data, and develop informed recommendations that shape or support the client's business strategy.
• Act as a technical consultant for the client.
• Work as a key member of an Agile Team.

• Own the end-to-end analytics data lifecycle: event taxonomy and tracking plan → ingestion → transformations → semantic layer/KPI definitions → dashboards
• Partner with product and client engineers to define and implement high-quality event instrumentation (naming conventions, properties, versioning)
• Build and maintain scalable, maintainable ELT/ETL pipelines, ensuring correctness, observability, and cost efficiency
• Design and implement data models (e.g., sessions, funnels, cohorts, monetization, retention) that support product decision making
• Define and enforce KPI definitions and metric governance to prevent metric drift and conflicting dashboards
• Implement data quality checks, anomaly detection, and monitoring (freshness, completeness, validity) with clear alerting
• Improve data discoverability via documentation, lineage, and clear datasets ownership
• Support experimentation and measurement: power A/B test analysis datasets and reliable experiment exposure logs (where applicable)
• Collaborate with stakeholders to prioritise the analytics roadmap and unblock critical product questions

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