Lead Data Engineer

About Cloudwick

Cloudwick modernizes how government works with data. Our firm belief is that fixing the underlying data is critical to every modernization effort. You have to get the data right first. Our product, Amorphic, provides a complete suite of tools to do exactly that, built by people with deep data and government expertise. We work with state health, education, environmental and public safety agencies on programs where the data has to be right because people depend on what it decides.

Position Overview

Our data engineers build customer data platforms on Amorphic, our data platform on AWS. Every Amorphic release changes something about how we build, and our engineers need to know what changed, which patterns still hold, and how to get a new joiner productive in their first weeks.

You will keep the Data Engineering practice current on the Amorphic platform. You publish what each release changes for delivery, keep our onboarding paths and training library current, and refresh our delivery pattern pages to the standard. This work covers the whole practice, not one project.

This is an individual contributor role with no direct reports. You work with the Staff Data Engineer, the Practice Lead, the pod leads in the US and India, and our DevOps team.

Key Responsibilities

• Release Readiness: Own release readiness after handover from the Staff Data Engineer. Prepare release guidance and proposed re-test patterns for Staff review and scope agreement. Publish within two weeks of each release demo and re-verify within 30 days. Record the tested version, results and open limitations; notify affected delivery teams and escalate access or review delays to the Practice Lead.

• Training Library: Maintain internal recordings with product versions and status. Add release demos and deep-dives, flag outdated material identified by the Staff Data Engineer, and track missing recordings with their owners.

• Delivery Patterns: Update pattern pages for the current Amorphic release, including tested reference implementations using synthetic data, checklists and support runbooks. Obtain Staff Data Engineer approval, with the Practice Lead approving the first two pages and disputed content.

• Code Reviews: Act as second reviewer for pipelines and CI/CD changes on assigned engagements, within permitted access. Escalate platform gaps to the Staff Data Engineer; deployment approval remains with the designated engagement owner.

• Customer Engagements: Spend approximately 50% of time on approved engagements as a hands-on lead or reviewer. Assignments must permit work from India, and access to customer systems, data, code and materials requires applicable approvals; non-production environments do not automatically grant access.

• Onboarding: Keep engineer and contractor onboarding paths current with each release, including day-one access requests. Review each new joiner's hands-on exercise.

Required Qualifications

• Bachelor's degree in computer science, engineering or a related field, or equivalent practical experience.
• Roughly 8 or more years of data engineering experience, including time as the technical owner of a project.
• Production data pipelines on AWS: S3, Glue or Spark, Athena or Redshift, and Lake Formation or equivalents, including incremental loads or change data capture, in Python and SQL.
• Documentation, runbooks or onboarding material you wrote that other engineers used, and how you kept it current.
• Reviewing other engineers pipeline code, including a change you asked for and why.
• Clear written English for engineers and for support staff.
• Delivery on a customer engagement, in consulting or professional services, with customer-facing duties.
• We will ask you for a specific example of each of these. A description is enough; we never ask for confidential material from a current or former employer. No certification is required.

Preferred Qualifications

• Hands-on work on a managed or opinionated data platform built on AWS (for example Lake Formation governed data lakes, Glue Studio, Databricks or Snowflake on AWS), where you built within the platform's conventions rather than around them.
• Upgrading or re-testing pipelines across platform releases.
• Infrastructure as code and CI/CD for data pipelines, such as AWS CDK or CloudFormation.
• Running recorded technical walkthroughs or internal training sessions.
• Public-sector or regulated data programs.
• AWS Certified Data Engineer, Associate.