• Design and implement scalable data models, storages and pipelines for critical product features • Drive the evolution of our data platform to meet future needs of real-time big data processing • Create and evolve architectural roadmaps for the data platform • Turn product requirements into viable and scalable data architectures and implementations • Act as a bridge between data science, product engineering, and stakeholders • Establish and follow data governance frameworks, standards, and best practices • Mentor senior engineers and architects on complex data modeling and distributed system challenges

Role Description Step into a role where you don’t just work with data—you bring it to life. You’ll help design, build, and maintain powerful, scalable data pipelines and platforms that keep our data easy to find, trustworthy, and ready for real-world use. Get hands-on with cutting-edge tools like Databricks and modern cloud technologies as you automate ETL processes, uncover insights from rich geospatial data, and champion best practices in metadata, testing, and governance. You won’t just be part of the team—you’ll be right at the heart of it, helping turn our data strategy into reality and shaping the next generation of our data lakehouse. Responsibilities - Build and Maintain - Design, build, and run robust data pipelines using Databricks and AWS, transforming large-scale geospatial datasets into high-quality, usable data. - Automate core ETL processes to enable smooth, reliable, and scalable data flows across the organisation. - Play a key role in shaping and delivering our medallion architecture, ensuring well-structured bronze, silver, and gold layers that power our data products. - Support the development of metadata frameworks and data cataloguing, making our data easier to find, understand, and trust. - Collaborate and Support - Partner with senior engineers and product teams to deliver data products that are not just reliable, but genuinely useful and easy to work with. - Get stuck into code reviews—sharing ideas, learning from others, and helping the whole team level up. - Be an advocate for best practices in testing, governance, and documentation, helping to build a strong and consistent data culture. - Maintain Excellence & Innovate - Contribute to automated data testing frameworks that keep data accurate, consistent, and geospatially sound. - Spot opportunities to make things faster and better—optimising pipelines and queries with techniques like spatial indexing. - Keep everything aligned with security, licensing, and governance standards—no shortcuts. - Stay curious and proactive, exploring new tools and smarter ways of working to keep improving how we handle data. Qualifications - Data Engineering & ETL: Skilled, Essential - Cloud Platforms (Databricks/AWS): Skilled, Essential - Programming (Python): Skilled, Essential - SQL & Spatial Databases: Skilled, Essential - Data Transformation: Skilled, Essential - Geospatial Tools: Competent, Desirable - Data Visualisation / BI: Competent, Desirable Our Culture in Action - Addresscloud 8 Core Elements - Customer Focus - Growing Yourself and Others - Innovation by Nature - Organisational Maestro - All-in Commitment - High-Quality/High-Impact Work - Teamwork & Collaboration - Narrative Advantage

Role Description We are looking for a freelance Data Engineer focused on data integration to build and maintain the pipelines that connect our many source systems into a unified, trustworthy data foundation. You will design ETL/ELT processes, integrate APIs and third-party platforms, model data for reporting, and ensure data quality across the organization. This is a fully remote role working closely with analytics, finance, and engineering stakeholders. Key Responsibilities - Design, build, and maintain scalable ETL/ELT pipelines that move and transform data between systems. - Integrate data from diverse sources — APIs, databases, SaaS platforms, flat files, and spreadsheets — into a central warehouse. - Develop and maintain connectors and reconciliation logic across business systems (e.g., project management, time-tracking, finance, and invoicing tools). - Model and structure data for analytics, reporting, and downstream applications. - Implement data validation, quality checks, monitoring, and alerting to ensure accuracy and reliability. - Optimize queries, storage, and pipeline performance for cost and speed. - Document data flows, schemas, mappings, and transformation logic. - Collaborate with analysts, finance, and engineering teams to understand requirements and deliver clean, usable datasets. - Support data governance, security, and privacy best practices. Qualifications - 3+ years of experience in data engineering, data integration, or a closely related role. - Strong SQL skills and experience with relational databases (PostgreSQL, MySQL, SQL Server). - Proficiency in Python (or a comparable language) for data processing and automation. - Hands-on experience building ETL/ELT workflows and orchestration (e.g., n8n, or similar). - Experience integrating REST APIs, webhooks, and third-party SaaS data sources. - Experience with data warehouses (BigQuery, Snowflake, or similar). - Understanding of data modeling, warehousing concepts, and data quality practices. - Comfort working with messy, real-world data across formats (CSV, Excel, JSON, XML). - Strong problem-solving skills and clear communication in a remote team. Nice to Have - Experience with cloud data platforms (AWS, or Azure). - Exposure to finance or operational data and cross-system reconciliation. - Experience with BI/visualization tools (Looker, Power BI, Tableau). - Experience in an agency or multi-entity, multi-currency environment. Benefits - Fully remote working with flexible hours. - A collaborative team spanning engineering, analytics, and finance. - Competitive salary and benefits package. How to Apply Submit your CV along with examples of pipelines, integrations, or data projects you have delivered.

• Design the lakehouse: Apache Iceberg (or similar technology) on object storage, a catalog for table management and per-bank isolation, dbt models, and a query engine • Build secure, least-privilege ingestion from bank systems — log-based CDC where permitted, with query-based and batch/SFTP fallbacks, plus an in-bank collector pattern • Own data modeling for the semantic and metric layer (deposits, concentration, uninsured exposure, asset quality, and peer groups) • Handle schema drift, data quality, and reconciliation; make ingestion observable and recoverable • Partner with the AI/ML team on the structured-query path and with Security on PII classification at landing, in alignment with regulatory data-handling requirements • Document data lineage, transformation logic, and access controls to support audit and exam readiness • Define and enforce data contracts, quality thresholds, and alerting for pipeline failures