• ETL/ELT Pipeline Development:
• Architect, develop, and operate scalable data pipelines in Python using frameworks such as Apache Airflow, AWS Glue, or similar.
• Ingest and transform data from internal sources, microservices, and third-party APIs (REST, streaming, webhooks).

• Data Modeling & Warehousing:
• Design and maintain dimensional and normalized schemas in cloud data warehouses (AWS Redshift, Snowflake, or equivalent).
• Optimize table structures, partitioning, and indexing for performance and cost efficiency.

• Third-Party Integrations:
• Build and manage robust, fault-tolerant integrations with external systems (payment gateways, identity providers, data vendors).
• Develop monitoring, retries, and alerting to ensure integration reliability.

• Data Quality & Governance:
• Implement data validation, anomaly detection, and reconciliation processes to guarantee accuracy.
• Collaborate with Security and Compliance teams to enforce data governance, encryption, and access controls.

• Collaboration & Enablement:
• Partner with Analytics, ML, and Product teams to translate requirements into data solutions.
• Provide self-service data access (views, dashboards) and documentation for stakeholders.

• Performance & Cost Optimization:
• Monitor and tune pipeline and warehouse performance; identify opportunities to reduce AWS spend.
• Introduce caching, batching, and parallelism as appropriate for large-scale workloads.

• Innovation & Tooling:
• Evaluate and prototype emerging data technologies (Spark, Kafka, dbt, data mesh patterns).
• Leverage AI/ML tools to automate repetitive data tasks or anomaly detection.

Required Qualifications

• Experience: 7+ years in data engineering or analytics engineering roles.
• Core Language: Expert-level Python for ETL scripting, API clients, and automation.
• Cloud Proficiency: Hands-on with AWS data services (S3, Glue, Redshift, EMR, Lambda) and infrastructure-as-code (Terraform or CloudFormation).
• SQL & Data Modeling: Deep expertise designing relational schemas, writing complex SQL, and building data marts.
• Pipeline Frameworks: Proven experience with Apache Airflow, AWS Glue, or equivalent orchestration tools.
• Integration Skills: Solid background integrating and transforming data from third-party APIs, streaming platforms, and message queues.
• Regulatory Compliance: Familiarity with data handling requirements in HIPAA, FedRAMP, and SOC-2 environments.
• Collaboration: Strong communication skills; able to partner effectively with cross-functional teams.

Preferred Qualifications

• Big Data Technologies: Experience with Apache Spark, Kafka, Kinesis, or similar.
• Modern Transform Tools: Proficiency with dbt, Delta Lake, or Iceberg for versioned tables and transformations.
• Containerization & Orchestration: Knowledge of Docker and Kubernetes for data workloads.
• Machine Learning Pipelines: Exposure to MLOps frameworks and feature stores.
• Healthcare & Government Domain: Prior work on healthcare analytics or government data projects.
• Open-Source Contributions: Engagement with data-engineering or analytics OSS communities.

Core Competencies & Expectations

• Hands-On Contributor: You dive into code and configurations, continuously shipping reliable data solutions.
• Data Quality Champion: You obsess over accuracy, completeness, and timeliness of data.
• Problem Solver: You break down complex data challenges into clear designs and implementations.
• Collaborative Mindset: You communicate clearly, document thoroughly, and empower others with data.
• Continuous Learner: You stay current with evolving data architectures and share insights with the team.

What We Offer

• Competitive salary and equity packages
• Comprehensive health, dental, and vision benefits
• Monthly Wellness Stipend
• Generous PTO

If you’re passionate about building robust data foundations that drive mission-critical insights and workflows, we’d love to talk. Please apply with your résumé and examples of your most impactful data-engineering projects.