• Build, optimize, and maintain data pipelines that power our business • Define and build out abstracted reusable data sets to be used for Business Intelligence, Marketing, and Data Science Research • Design, build, and evangelize a federated data validation framework to be used to monitor potential data inconsistencies • Protect our users’ privacy and security through best practices

Chainalysis’ Enterprise Data & Analytics team is responsible for generating insights from Chainalysis’ corporate functions and GTM functions. We do this by ingesting, storing and enabling consumption of trusted Account Journey data, metrics and insights across the B2B Enterprise Funnel. We design and implement the critical infrastructure that unifies data across business systems, empowering analytics, operational excellence, and advanced AI initiatives. Our mission is to deliver secure, scalable, and reliable data solutions that enable every part of Chainalysis to thrive in an ever-evolving technology landscape. As a Senior Data Engineer, Enterprise Data Analytics - you work across low-code platforms and custom cloud-native services, leveraging your software engineering background and DevOps experience. This role requires strong problem solving skills, and the ability to partner with key stakeholders and drive strategic collaboration to shape the future of our enterprise data platform. You'll work across low-code platforms and custom cloud-native services. In this role, you’ll: - Design and implement robust, scalable integrations between Databricks and key enterprise business systems (Netsuite, Workday, SalesForce) - Translate business requirements into technical specifications and integration workflows - Develop, and maintain automated data pipelines using a combination of low-code tools and custom AWS solutions (Lambda, Step Functions, S3, CloudWatch, VPCs, IAM) - Own the provisioning and management of infrastructure for integrations by applying Infrastructure as Code principles with Terraform - Mentor engineers and champion best practices in software engineering, source control (Gitflow), and DevOps workflows within Github repositories - Collaborate closely with data scientists, product managers, and stakeholders to ensure integrations deliver transformative business value - Proactively monitor, troubleshoot, and ensure reliability and observability of integration solutions across distributed systems We’re looking for candidates who have: - Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent professional experience - Minimum 6+ years of hands-on experience in software engineering - Hands-on expertise with Terraform and modern DevOps practices, including CI/CD and infrastructure lifecycle automation - Proficiency in Python or Node.js - AWS experience: Lambda, Step Functions, S3, CloudWatch, VPCs, IAM. - Hands-on experience with Databricks, Snowflake or similar for data warehousing, modeling, or pipeline optimization. - Experience with modern source control workflows (Gitflow), using Github You might also have: - Hands-on experience with Databricks or Snowflake, especially in data warehousing, modeling, or pipeline optimization. About Chainalysis Blockchain technology is powering a growing wave of innovation. Businesses and governments around the world are using blockchains to make banking more efficient, connect with their customers, and investigate criminal cases. As adoption of blockchain technology grows, more and more organizations seek access to all this ecosystem has to offer. That’s where Chainalysis comes in. We provide complete knowledge of what’s happening on blockchains through our data, services, and solutions. With Chainalysis, organizations can navigate blockchains safely and with confidence. You belong here. At Chainalysis, we believe that diversity of experience and thought makes us stronger. With both customers and employees around the world, we are committed to ensuring our team reflects the unique communities around us. We’re ensuring we keep learning by committing to continually revisit and reevaluate our diversity culture. We encourage applicants across any race, ethnicity, gender/gender expression, age, spirituality, ability, experience and more. If you need any accommodations to make our interview process more accessible to you due to a disability, don't hesitate to let us know. You can learn more here. We can’t wait to meet you.

About Cylinder Founded in 2021 and offered as an employer benefit, Cylinder (formerly Vivante Health) delivers personalized, clinician-backed care to those suffering from digestive health issues through its virtual health platform. Cylinder’s tech-forward, human-first program connects members to the right level of support at the right time through an easy interface, bringing the healthcare ecosystem together to align around member needs. Cylinder’s dedicated care teams help members proactively address their gut health from every angle – decreasing costs, reducing absenteeism, and improving their quality of life, at scale. Cylinder’s vision is to create a world where people receive convenient, personalized, and affordable access to the quality digestive healthcare they need to improve their overall health. Position Summary We’re seeking a highly experienced Senior Data Engineer to lead our data infrastructure initiatives and mentor our growing engineering team. As a Senior Data Engineer, you will architect and optimize enterprise-scale data solutions while driving technical strategy across the organization. You'll play a pivotal role in establishing data engineering best practices and building systems that can scale with our rapidly growing member base. Why is this a great opportunity for a data engineer? You'll be joining at a pivotal moment where your technical leadership will directly shape our data strategy. The problem domain offers unique challenges - integrating previously disconnected healthcare data sources, building sophisticated GI patient models, and discovering insights that tangibly improve patient outcomes. As a senior contributor, you'll have significant autonomy in technical decisions and the opportunity to build a world-class data engineering function from the ground up. Key Leadership Responsibilities: - Architect and design scalable, fault-tolerant data pipelines for complex healthcare data ingestion, transformation, and reporting - Lead technical decision-making for data infrastructure and tooling selection - Mentor junior engineers and establish engineering best practices across the team - Drive cross-functional collaboration with Data Science, Business Intelligence, and Product teams - Champion data quality initiatives and establish robust monitoring and alerting systems - Design and implement solutions for complex distributed system challenges - Evaluate and implement emerging technologies to maintain our competitive edge Responsibilities Include: - Design and implement enterprise-grade data architectures with emphasis on scalability, reliability, and maintainability - Lead code reviews and establish coding standards that promote excellence across the engineering organization - Drive strategic partnerships with product stakeholders, translating complex business requirements into elegant technical solutions - Own end-to-end delivery of critical data infrastructure projects - Establish comprehensive testing strategies, documentation standards, and operational procedures - Continuously evaluate and optimize system performance, cost efficiency, and reliability - Serve as subject matter expert for healthcare data compliance and security requirements You'll be a good fit here if you bring: - Technical Leadership: You have a proven track record of architecting complex data systems and leading technical initiatives in fast-paced environments. - Healthcare Impact: You're passionate about leveraging data to improve patient outcomes and understand the unique challenges of healthcare data. - Security Excellence: You have deep expertise in healthcare data security, HIPAA compliance, and implementing robust data governance frameworks. - Collaboration & Mentorship: You excel at leading cross-functional teams and developing junior talent. - Innovation Mindset: You stay current with emerging technologies and can evaluate their strategic fit for our organization. Desired Qualifications - Bachelor's degree in Computer Science, Engineering, or related field, or equivalent professional experience - 5+ years of demonstrated experience architecting and building production data pipelines and MLOps environments - Expert-level proficiency in SQL and Python with demonstrated experience in performance optimization - Deep expertise with modern data stack technologies including dbt, Airflow, BigQuery, and cloud-native solutions - Extensive experience with major cloud platforms (AWS, GCP, or Azure) including infrastructure-as-code practices - Proven track record working with healthcare, medical, or population health data - Outstanding communication and leadership skills with experience mentoring engineering teams - Experience establishing data governance and compliance frameworks in regulated industries Preferred Qualifications - Advanced degree in Computer Science, Data Engineering, or related field - Extensive experience with Google Cloud Platform, including BigQuery, Vertex AI, IAM, Pub/Sub, Dataflow, and Cloud Composer - Expert-level experience with Terraform and infrastructure automation - Experience with real-time streaming data architectures - Track record of successfully scaling data systems in high-growth startup environments - Published thought leadership or contributions to open-source data engineering projects Cylinder is an equal opportunity employer. We believe that creating safe spaces where everyone can be their authentic selves is key to a successful team. We welcome and embrace all identities, cultures, and backgrounds.

• Build and maintain observability frameworks to monitor the health, performance, and reliability of data and analytic platforms • Implement tools for real-time monitoring, logging, and alerting to ensure proactive issue detection and resolution • Analyze system metrics and logs to identify trends, anomalies, and areas for improvement • Design and implement intelligent automation solutions to streamline data and analytic processing workflows and platform operations • Develop scripts, tools, and frameworks to automate repetitive tasks and improve system efficiency • Collaborate with cross-functional teams to identify automation opportunities and implement solutions • Ensure high availability and fault tolerance of the platform through robust monitoring and automation strategies