Senior Data Platform Developer

Job requirements

• 6+ years of experience as a data engineer, analytics engineer, data platform developer, or similar role.

• Strong experience designing, building, and maintaining production-grade data pipelines and data platform infrastructure.

• Experience deploying data products, analytics assets, or data platform capabilities into production.

• Experience working cross-functionally with technical and non-technical stakeholders to understand business needs and translate them into scalable technical solutions.

• Demonstrated experience leading complex technical projects from discovery through delivery.

• Experience mentoring, coaching, reviewing work, or otherwise helping other team members grow technically.

• You excel at untangling vague, complex data problems, distilling them into practical, high-quality execution plans while creating clarity for the broader team.

• You possess strong technical judgment and make sound cross-domain trade-offs. You proactively hunt down platform risks, technical debt, and governance gaps before they scale.

• You naturally multiply the effectiveness of the engineers around you through collaborative technical guidance, code reviews, and robust documentation.

• You can effectively drive buy-in for platform roadmaps and confidently represent technical decisions to both internal leadership and external entities (e.g., auditors or compliance).

• Technology Stack: We use BigQuery, dbt, Airflow, and Airbyte as our data warehousing technologies and SQL + Python as our programming languages

• Custom Tooling & Plugins: Experience developing Agentic Workflows, developing Specs/Skills for IDEs to automate the development of data pipelines & infrastructure

• Container Orchestration: Hands-on experience with Kubernetes for deploying, scaling, and managing containerized data applications and MLOps workflows.

Job responsibilities

• Drive and own the advancement of Helcim’s MLOps and AI Gateway infrastructure, actively pushing these capabilities forward to support production AI and machine learning models.

• Lead the end-to-end design of scalable data pipelines (structured and semi-structured) to power ML models, AI systems, BI, and operational decision needs.

• Continually improve Helcim’s self-managed data platform for high performance, reliability, and observability, while proactively mitigating technical risks.

• Expand internal AI tooling and automate data governance to ensure data quality, privacy, security, and trust.