What You'll Bring to the Team:Lead the design and delivery of large, cross-functional data engineering initiatives. Build scalable, reliable, secure, and well-governed data solutions that support analytics, reporting, business applications, and emerging AI/ML use cases.
You Will:- Lead the design and implementation of scalable batch and real-time data pipelines.
- Define and enforce standards for data modeling, reliability, testing, documentation, observability, security, and governance.
- Drive root-cause analysis, performance optimization, and remediation of systemic data and platform issues.
- Partner with product, engineering, analytics, data science, and business leaders on roadmap and prioritization.
- Engineer scalable data pipelines and data models across PostgreSQL, MongoDB, Snowflake, and AWS using modern ETL/ELT and DataOps practices.
- Develop and maintain transformation workflows using dbt, including reusable models, testing, documentation, and lineage.
- Design and support real-time and event-driven data pipelines using Kafka and related streaming technologies.
- Implement data integration and Master Data Management (MDM) practices to improve data consistency, quality, governance, and integrity across enterprise systems.
- Build curated datasets and semantic layers that support analytics and business intelligence platforms such as Amazon QuickSight and Tableau.
- Develop data solutions that support machine learning, AI, and Generative AI use cases, including preparation of structured and unstructured data.
- Support RAG and LLM-based applications through data ingestion, transformation, enrichment, embeddings, vector search, and retrieval pipelines.
- Apply data quality, lineage, security, governance, and access-control practices to data used across analytics and AI/ML applications.
- Leverage AI-assisted development tools such as Claude Code, Cursor, GitHub Copilot, or equivalent to improve engineering productivity, testing, documentation, and code quality.
- Mentor engineers across multiple levels and contribute to technical strategy, architecture reviews, code reviews, and engineering best practices.
An Ideal Candidate is/has:Experience- 5+ years relevant experience
- A bachelor's or master's degree in computer science, data science, information science or related field, or equivalent work experience
- Deep, hands-on expertise in data engineering, cloud data platforms, data integration, and data modeling, with practical experience supporting analytics and AI/ML use cases.
Data Engineering & Databases- PostgreSQL / SQL: Advanced SQL development, query optimization, indexing, performance tuning, backup, recovery, and security.
- MongoDB: NoSQL data modeling, query optimization, indexing, performance, and administration concepts.
- Snowflake: Data warehousing, performance optimization, scalable data processing, security, and cost optimization.
- dbt: ELT transformation frameworks, modular data models, testing, documentation, lineage, and CI/CD integration.
- ETL/ELT: Design, development, optimization, monitoring, and troubleshooting of enterprise data pipelines.
- Data Modeling: Dimensional, analytical, transactional, and normalized data models.
- Master Data Management (MDM): Data standardization, master/reference data, data quality, governance, and enterprise data integration.
- Data Quality & Governance: Data validation, integrity, lineage, metadata, observability, and governance.
- Workflow & Orchestration: Airflow or equivalent workflow orchestration technologies.
- Streaming: Kafka and event-driven data processing.
AWS & Cloud Data EngineeringHands-on experience with the
AWS ecosystem and cloud-native data engineering, including relevant services such as:
- Amazon S3
- Amazon MSK / Kafka
- AWS Lambda
- Amazon RDS
- Amazon Aurora
- AWS IAM
- Amazon QuickSight
Ability to design secure, scalable, highly available, and cost-effective cloud data solutions using AWS services.
Analytics & Business Intelligence- Experience preparing and serving trusted, governed datasets for BI and analytics.
- Amazon QuickSight - dashboards, datasets, data preparation, and analytics.
- Tableau - dashboards, reporting, data sources, and analytical data models.
- Strong understanding of the relationship between data engineering, semantic models, BI, and business reporting requirements.
AI/ML & Generative AI - Working Experience- Practical experience preparing and managing data for machine learning and AI applications.
- Understanding of data preparation, transformation, feature engineering, and training datasets.
- Familiarity with LLM data pipelines and the role of data engineering in supporting LLM-based applications.
- Understanding of AI/ML data governance concepts, including data quality, lineage, privacy, security, and access controls.
- Experience using AI-assisted development tools such as Claude Code, Cursor, GitHub Copilot, or equivalent.
- Ability to collaborate effectively with data scientists, ML engineers, and application engineers on AI/ML data requirements.
- Exposure to RAG and LLM-based applications
DevOps, DataOps & Engineering Practices- DataOps and CI/CD practices for data pipelines and transformations.
- Automated data testing and quality validation.
- Git-based development and code review practices.
- Infrastructure as Code using Terraform.
- Monitoring, observability, logging, alerting, and production support.
- Strong documentation and engineering compliance practices.
Core Strengths- Communication - Advanced
- Ownership - Advanced
- Accountability - Advanced
- Technical Leadership
- Problem Solving
- Cross-functional Collaboration
- Mentoring & Knowledge Sharing
- Continuous Improvement
Bonus Points if you have:- AWS, Snowflake, MongoDB, PostgreSQL, or dbt certifications
- Experience with Databricks, Spark, Delta Lake, or other lakehouse technologies
- Experience with advanced Kafka/event-streaming architectures
- Experience with vector databases and vector search
- Exposure to MLOps and machine learning data pipelines
- Experience with AWS AI/ML services such as Amazon Bedrock or SageMaker
- Experience with data observability platforms
- Experience implementing enterprise data governance and MDM solutions
- Experience supporting large-scale enterprise analytics and BI environments
What’s in it for you?- Do well. We offer a competitive compensation package, medical and dental insurance, and office space in the heart of the city.
- Do good. We insist that health technology is the highest calling for software development. We pride ourselves on working on something bigger than ourselves; helping advance cures and therapies.
- Make the leap. Join our high-output culture to create innovative, modern, and purposeful software solutions.
