Role Description We are looking for a Data Engineer with exceptional SQL skills to join our growing team at uMotif. This role is primarily focused on writing, optimizing, and maintaining complex SQL across our clinical data infrastructure on AWS. You will be the go-to person for query performance, data extraction, and SQL-driven pipeline development — ensuring our clinical and product teams always have fast, reliable access to the data they need. You will work closely with TechOps, DevOps, Engineering, and Clinical Operations to build well-crafted SQL solutions that underpin uMotif’s patient engagement and clinical trial platforms. Please note, this is a remote-working role; however you will need to align with east-coast (EST) working hours to be able to liaise with the team in the UK time-zone (BST). What will you do? - SQL Development & Optimization - Write and maintain complex SQL queries across large-scale clinical datasets, including multi-table joins, window functions, CTEs, and subqueries. - Diagnose and tune slow-running queries using execution plans, index analysis, and query profiling tools — delivering measurable performance improvements. - Establish and enforce SQL best practices, coding standards, and review processes across the data team. - Optimize SQL for cost and performance — with a deep understanding of how the complete system handles query execution. - Build and manage indexes, partitioning strategies, and materialized views to support performant analytical and operational queries. - Data Pipeline Development - Design and build ELT/ETL pipelines with SQL at their core, leveraging AWS services such as: - AWS Aurora for structured data processing - AWS Lambda and Step Functions for orchestration and transformation triggers - Write transformation logic using dbt, including tests, documentation, and lineage tracking. - Ensure pipelines are performant, reliable, and well-monitored — with clear alerting when things go wrong. - Analytics & Reporting Enablement - Build clean, well-documented SQL datasets and semantic layers that empower self-serve analytics across clinical and product teams. - Partner with TechOps and clinical stakeholders to translate reporting requirements into robust, reusable SQL data products. - Support dashboard and reporting tools including Grafana and Amazon QuickSight with optimized underlying queries. - Data Quality & Governance - Implement SQL-based data quality checks and validation frameworks across critical pipelines. - Support data cataloging, lineage tracking, and access control in line with healthcare data standards. - Assist with compliance requirements for clinical trial data, including audit trails and row-level security where needed. - Collaboration & Continuous Improvement - Participate actively in code reviews, with a particular focus on SQL quality, readability, and performance. - Mentor junior engineers and analysts on SQL patterns, optimisation techniques, and data engineering fundamentals. - Contribute to technical documentation, runbooks, and data engineering best practices. - Drive root cause analysis for data incidents and improve pipeline reliability over time. Qualifications - 4+ years of experience in data engineering or a closely related role, with SQL as a core daily skill. - Demonstrable expertise in writing complex, production-grade SQL — including window functions, recursive CTEs, lateral joins, and advanced aggregations. - Proven track record of query optimization: reading execution plans, diagnosing bottlenecks, and delivering significant performance improvements. - Strong hands-on experience with AWS data services, particularly Aurora, Redshift, Athena, and S3. - Experience building ELT/ETL pipelines at scale, with SQL transformation at their core. - Proficiency in dbt for data transformation, testing, and documentation. - Experience with Python for pipeline orchestration and data processing tasks. - Familiarity with workflow orchestration tools such as Apache Airflow or AWS MWAA. - Understanding of data quality principles, access control, and governance (e.g. AWS Lake Formation). - Experience working in a GitLab or similar CI/CD environment. - Strong analytical mindset, attention to detail, and excellent communication skills. Technical Skills - Core SQL & Data Tools - SQL (expert level) — Aurora/PostgreSQL, Athena/Presto, Redshift SQL - dbt (data build tool) - Python - Apache Airflow / AWS MWAA - AWS Data Services - AWS Aurora (PostgreSQL-compatible) - Amazon CloudWatch (Data Insights, Performance Insights) - AWS Lambda & Step Functions - Amazon S3 - Amazon Redshift — including query tuning, WLM configuration, and distribution strategies - Amazon Athena — federated queries, partitioning, columnar formats (Parquet, ORC) - AWS Lake Formation - AWS Glue (supporting role) - Other Tools - GitLab CI/CD - Amazon QuickSight / Grafana - Terraform (nice to have) Other Important Skills - Strong analytical and troubleshooting capabilities with a systematic approach to query debugging. - Ability to work independently and collaboratively across cross-functional teams. - Strong documentation and communication skills — able to explain SQL logic and data decisions to non-technical stakeholders. - Continuous improvement mindset with a focus on data reliability, performance, and quality. - Ability to manage multiple priorities in a fast-paced, mission-driven environment. Nice to have - Experience in healthcare, life sciences, or clinical trials data environments. - Familiarity with healthcare data standards such as HL7 or FHIR. - AWS certifications such as AWS Certified Data Engineer – Associate or AWS Certified Solutions Architect. - Knowledge of Infrastructure as Code using Terraform. - Exposure to streaming data pipelines using AWS Kinesis or Apache Kafka.

• Design, build, and maintain robust, scalable data pipelines and ETL/ELT workflows • Develop and manage data warehousing solutions to support business intelligence and analytics needs • Ensure data quality, integrity, and availability across all data systems and sources • Collaborate with data scientists, analysts, and product teams to understand and fulfill data requirements • Optimize query performance and database architecture for speed and efficiency • Monitor, troubleshoot, and resolve data pipeline failures and incidents • Implement and enforce data governance, security, and compliance best practices • Document data models, processes, and infrastructure to support team knowledge sharing

Role Description We are looking to hire a Data Migration Specialist who can facilitate the transfer of data from customers’ prior software to our platform. You’ll collaborate closely with onboarding, support, product, and engineering teams to translate customer data from legacy systems and spreadsheets into structured, clean, and usable datasets. This is a customer-facing role requiring technical skill, attention to detail, and strong communication abilities. Our small and dynamic team (currently 40 people) reports directly to the VP of Customer Success. Duties and Responsibilities - Data Analysis and Mapping - Review customer data (CSVs, spreadsheets, software databases) to assess structure, quality, and completeness - Map legacy data fields to our platform’s schema using internal tools, AI (Chat GPT) or custom scripts - Migration Execution - Transform, clean, and normalize customer data - Import data into Gymdesk customer environments and validate against expected outputs - Perform iterative migrations and troubleshoot errors during the process - Product Expertise and Manual Data Entry - When required, fill any migration gaps with manual data entry - Become an expert in Gymdesk’s product, enabling the ability to set up configurations on behalf of a customer - Customer Communication - Coordinate with onboarding specialists and customers to collect requirements and data formats - Provide status updates, clarify data issues, and assist customers in understanding field-level discrepancies - Collaboration and Process Improvement - Work with product & engineering to report bugs or suggest tooling enhancements - Document migration processes and maintain internal guides for repeatable success Qualifications - 3+ years of experience in data migration, data analysis, business intelligence, or a similar technical role - Proficiency in handling spreadsheets, CSV files, text encoding, date formats, and data cleansing techniques - Experience working with SaaS platforms, CRM, ERP, or customer-facing data workflows - Strong analytical and problem-solving skills with exceptional attention to detail - Excellent written and verbal communication skills - Experience working in customer-facing or onboarding roles is a plus - Knowledge of SQL and at least one scripting language (e.g., Python) is preferred - Understanding of data privacy regulations (e.g., GDPR) is a plus - Experience building or working with internal tools for data migration - Exposure to project management and collaboration tools (e.g., Notion, Jira, Slack) Success Metrics - Percentage of migrations completed within the designated SLA (e.g., 5–7 days for Tier 1 clients) - Accuracy of migrated records and field mappings - Customer satisfaction scores (CSAT, NPS) post-onboarding - Positive internal team feedback on collaboration and adherence to process Benefits - Permanent remote work flexibility - Paid Time Off - Health Maintenance Organization (HMO) coverage - Annual performance bonuses - Dedicated coaches offer an extra channel of support and skill-building - Opportunities for professional growth

• Design and build scalable data quality frameworks and validation systems that process financial data at enterprise scale and across diverse datasets • Own services end-to-end, from architecture and implementation through deployment and operational support • Build tools and interfaces that enable operations teams to efficiently identify, investigate, and resolve data issues • Design and deploy resilient, scalable cloud-native infrastructure on AWS using Terraform • Collaborate with cross-functional teams including product managers, data engineers, and operations stakeholders to deliver high-impact features • Work with modern data processing technologies like pySpark and Databricks to build, validate, and monitor data pipelines • Contribute to the broader Data Platform vision of making data discoverable, trustworthy, and actionable across Addepar