Data Engineer

About CorrDyn

CorrDyn has been in business for over ten years. We’re 20+ people across five countries, embedded with client teams to build pipelines, develop reports, and deliver analytics that inform real business decisions.

Our clients span industries, and every engagement brings a different domain, a different stack, and a different set of constraints. Most of our work comes in by referral and most clients stick around for years; we don’t churn through clients or through people.

We’re tech-agnostic and solution-oriented, remote-first, and direct. We value people who communicate clearly, ask good questions, and care about getting the details right.

The Role

We’re looking for a Data Engineer who can design and build cloud-native data pipelines, work across multiple cloud providers, and deliver production-quality data infrastructure for clients with very different needs.

This is a consulting role. You won’t own a single company’s data platform. Instead, you’ll work across multiple client engagements simultaneously, each with their own cloud environment, data sources, and business requirements. One week, you might be building an ELT pipeline in dbt against Snowflake. The next, you’re setting up data ingestion through Estuary or Fivetran into MotherDuck, or standing up Databricks on AWS or Azure. That range of exposure is what separates this role from a typical data engineering position.

You’ll need to be comfortable gathering technical requirements from clients, proposing solutions, and then building them. The role is roughly 80% hands-on building and 20% project communication (client calls, internal syncs, written updates). You’ll participate in client meetings, translate business needs into technical architecture, and communicate progress clearly. But the core of the job is building things that work, scale, and don’t break.

We pay competitively within each region we hire in, scaled to skill and the value you deliver to clients rather than to a fixed title or band.

What You’ll Do

• Design, build, and maintain scalable ELT data pipelines using Python, SQL, and dbt
• Take a first pass at solution design under the guidance of a senior architect, incorporate feedback through one or two review rounds, and then build the solution. The architect owns the final architecture; you own the implementation, the monitoring, and the ongoing reliability.
• Deploy and manage pipelines across cloud environments including AWS, GCP, and Azure
• Work with cloud data warehouses like Snowflake, Databricks, BigQuery, and MotherDuck: configuring, loading, transforming, and optimizing data within each
• Set up and manage data ingestion using tools like Estuary, Fivetran, and custom Python-based connectors
• Implement monitoring, alerting, and data quality checks to keep deployed pipelines reliable
• Participate in technical discovery with clients to understand their data sources, processing needs, and business objectives
• Manage infrastructure using code (Terraform, Pulumi) and work with containerized workloads (Docker, Kubernetes) where appropriate
• Optimize pipeline performance, storage costs, and query efficiency across client environments
• Implement security best practices including role-based access control (RBAC), data encryption, and sensitive data handling
• Work alongside data analysts, analytics engineers, and client stakeholders to make sure data models meet business needs
• Contribute to CorrDyn’s internal engineering practices, including CI/CD automation, documentation, and knowledge sharing

What We’re Looking For

We hire on demonstrated capability, not on checking every box. If you can do most of what’s in the first list and some of what’s in the second, apply. We’d rather hear from you and figure out the fit together than have you self-select out.

What you’ll need:

• Experience building and deploying cloud-based data pipelines (1+ years, or equivalent demonstrated work)
• Proficiency in Python and SQL for data transformation and pipeline development
• Working knowledge of at least one major cloud platform (AWS, Azure, or GCP)
• Solid understanding of data modeling and ELT architectures for cloud data warehouses
• Ability to manage multiple projects simultaneously and communicate effectively in a remote environment

What’s a plus:

• Experience with dbt for data modeling and transformation
• Experience working with at least one major cloud data warehouse (Snowflake, Databricks, BigQuery, or MotherDuck)
• 2 to 4 years of data engineering experience across multiple cloud environments
• Experience with data ingestion platforms such as Estuary or Fivetran
• Hands-on experience administering and optimizing a cloud data warehouse (not just querying, but configuring, tuning, and managing)
• Experience with containerized workloads (Docker, Kubernetes)
• Experience with event-driven or streaming architectures (Kafka, Kinesis, Pub/Sub, Flink, Spark Streaming, or similar). Specific tooling varies by client, so familiarity with the patterns matters more than expertise in any single platform.
• Familiarity with infrastructure as code (Terraform, Pulumi) for cloud deployments
• Experience with workflow orchestration tools (Airflow, Prefect, Dagster)
• Experience with CI/CD pipelines for data workflows, including monitoring and logging frameworks
• Knowledge of cloud security best practices, RBAC, and data governance
• Prior experience in a consulting, agency, or multi-client environment
• Comfort communicating directly with client stakeholders to gather requirements and propose technical solutions

A Note on Leveling

We don’t use title bands. Compensation and project assignments scale with demonstrated skill and the billable level you operate at, which is reassessed as you grow. Engineers who deliver well move up quickly without waiting for a promotion cycle.

What Success Looks Like

First 30 days: You’ve set up your cloud environments, participated in technical discovery for at least one client, and delivered an initial pipeline to production with team support. You understand CorrDyn’s engineering frameworks and are contributing code via GitHub.

By 3 months: You’re independently building and optimizing data pipelines. You’ve deployed to at least one cloud environment using containerized or orchestrated workloads. You’re writing monitoring and alerting for your deployed solutions, participating in client meetings, and getting comfortable with infrastructure as code.

By 6 months: You own the data pipeline architecture for at least one client project. You’re implementing cost-effective, high-performance transformations in dbt, helping administer and optimize cloud data warehouse environments, and applying security best practices for access control and sensitive data handling.

By 12 months: You’re operating at a higher billable level, owning multiple client implementations, and contributing to the engineering practices the rest of the team uses (CI/CD patterns, dbt conventions, monitoring standards).

What Makes Someone Successful Here

We’re a small, high-trust team. Here’s what we’ve seen separate the people who do well from those who struggle:

Before any of that: we don’t care about credentials, background, or what you look like on paper. We care whether you can do the work, whether you’re honest about what you don’t know, and whether you’re someone the team actually wants to work with. Everything else is noise.

Solve the actual problem. You’ll encounter ambiguous requirements, messy source systems, and clients who don’t always know what they need. The best engineers here diagnose the real problem, propose a practical solution, and build it without waiting for someone to hand them a spec.

Adapt to the client’s world. Every engagement brings a different tech stack and a different set of constraints. You need to be comfortable working in environments you didn’t choose and finding the right solution within those constraints.

Communicate like a collaborator. You’ll participate in client calls, explain technical trade-offs in plain language, and keep stakeholders informed. We need engineers who build trust through transparency, not people who disappear into a terminal.

Take ownership. When you deploy a pipeline, you own it. That means monitoring it, fixing it when it breaks, and improving it over time. Reliability is a feature, not someone else’s job.

Be willing to stretch. Some projects might require you to step into an analytics engineering or even a light data science role. We provide support, but we need people who lean into unfamiliar work rather than away from it.

Why CorrDyn

Build breadth fast. You’ll work across more cloud platforms, data tools, and architectural patterns in a year here than most engineers encounter in three years at a single company.

Build on MotherDuck. We’re an active MotherDuck partner with a growing practice and live client workloads. You’ll work with a data warehouse most engineers have only read about, on real production problems, with a team that knows the platform deeply.

Actually remote. We don’t have an office. Work from wherever you work best, with a team built around asynchronous communication and clear expectations.

Build new things. You’re not maintaining legacy systems. You’re building data infrastructure from the ground up for organizations that need it to make better decisions.

Learn from experienced practitioners. You’ll get direct feedback from engineers who’ve built data platforms for companies ranging from big technology companies to biotech startups to sports organizations.

Be treated well. We care about your professional and personal growth. We’re building a team of people who want to be here, not a machine that burns through talent.

Working Hours

This is a full-time remote role on U.S. Central Time. We have team members across multiple regions; those outside the Americas work a CT-aligned schedule with flexibility when life requires it. Please confirm you’re able to work this schedule sustainably.

Travel is required occasionally but is minimal. Most people travel zero to two times per year for client site visits and CorrDyn offsites.

---

CorrDyn provides equal employment opportunities to all qualified applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic information, protected veteran status, or any other characteristic protected by applicable federal, state, or local law.