Data Platform Developer

Harley Ellis Devereaux•Los Angeles, CA

16h•Hybrid

About The Position

HED is hiring a Data Platform Developer to develop, evaluate, and improve the AI capabilities that support its people and practice. As part of the Data & AI team, the role operates and improves a modern lakehouse built on Databricks, Delta Lake, and Unity Catalog, connecting business systems to trusted analytics and emerging AI applications. This individual contributor role has meaningful ownership of production pipelines, platform health, data documentation, and continuous improvement. The developer partners with analysts, application developers, and business data owners to make information accessible, dependable, and actionable, and helps shape the safe use of AI-assisted tools to monitor and improve the platform. HED describes its culture as inclusive and integrated, valuing diverse perspectives, experience, abilities, and expertise. It emphasizes aspiration, embracing change and challenges, listening and learning from one another, continual learning, and collective growth. The position uses a hybrid work model with opportunities to work from home and regular in-office time. Offices provide a professional, open-space environment for collaborative and independent work. The role involves prolonged periods sitting at a desk and working on a computer, communicating in writing and verbally by phone, video conference, and in person, and visual acuity sufficient to perform the responsibilities, with or without reasonable accommodation.

Requirements

At least 3 years of experience building and supporting production data pipelines on a cloud data platform such as Databricks, Snowflake, Microsoft Fabric/Synapse, or BigQuery.
Strong SQL and Python skills, with experience using Spark or a comparable distributed data processing framework.
Practical experience troubleshooting production jobs, recovering or backfilling data, and participating in operational incident response.
Experience implementing monitoring, alerting, and data reliability practices.
Ability to write clear technical documentation and communicate effectively with technical colleagues and business stakeholders.
Ability and interest to learn HED’s specific tools and practices; direct experience with every technology listed is not required.
Prolonged periods of sitting at a desk and working on a computer.
Ability to communicate effectively in writing and verbally via phone, video conferencing, and in-person.
Visual acuity sufficient to perform the responsibilities of the position, with or without reasonable accommodation.

Nice To Haves

Experience with Delta Lake or Apache Iceberg, medallion architectures, Databricks Workflows, Unity Catalog, and Azure Data Lake Storage.
Familiarity with infrastructure as code, Git-based CI/CD, automated testing, data catalogs, lineage, and access governance.
Experience with Grafana, Prometheus, Loki, cloud cost optimization, AI-assisted development, or enterprise systems such as Workday, Deltek, and Dynamics.

Responsibilities

Develop, operate, and improve ingestion and transformation pipelines across Bronze, Silver, and Gold data layers, integrating sources such as Workday, Deltek, Dataverse, Microsoft Graph, and collaboration systems.
Administer and continuously improve the Databricks environment, including workflows, compute standards, data storage, access controls, and Unity Catalog governance.
Maintain version-controlled, reproducible platform configurations and deployment workflows; identify and resolve differences between source control and production environments.
Implement automated data quality checks and schema contract tests to protect governed datasets from unexpected changes.
Monitor pipeline freshness, availability, performance, and cost; maintain actionable alerts, dashboards, and operational runbooks.
Investigate and resolve production incidents, coordinate data recovery and backfills, document root causes, and implement preventive improvements.
Maintain technical lineage, source metadata, the data dictionary, and platform documentation; collaborate with business owners to confirm definitions and accountability.
Assess downstream impacts of schema changes, coordinate communications with data consumers, and support controlled changes across dependent systems.
Collaborate with analytics and AI development colleagues to deliver reliable, well-governed data products and evaluate AI-assisted operational workflows.
Contribute to platform architecture, technical standards, and future data-source onboarding as experience and ownership expand.