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.

Required Qualifications / Experience

• 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.

Preferred Qualifications

• 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.