Job Description
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Company Overview
A rapidly growing GridTech company is developing an AI-powered vertical SaaS platform designed to help electric utilities modernize grid operations, accelerate distributed energy resource (DER) interconnection, and unlock dynamic grid capacity.
Key Responsibilities
Customer Implementations (~80%)
- Lead end-to-end data implementation engagements with utility customers, from data requirements workshops through to live data flowing into the platform.
- Set up and configure data infrastructure according to established implementation processes.
- Map incoming customer data to platform schemas and develop code to transform customer datasets.
- Validate transformed data against established quality standards.
- Review incoming customer data against expected schemas and quality requirements.
- Provide clear and constructive feedback to customers regarding data quality and integration issues.
- Run and monitor data pipelines required to deliver successful implementations.
- Drive customer data deliveries through to completion.
- Triage and resolve implementation issues while maintaining clear communication with customers and internal stakeholders.
- Manage complex implementation situations involving incomplete data, changing priorities, tight timelines, and competing demands.
- Maintain momentum throughout customer implementation projects and ensure technical deliverables remain on track.
Product Support (~20%)
- Identify recurring patterns across customer engagements and communicate insights that can inform product direction.
- Partner with data engineering teams to productize implementation and transformation logic.
- Convert bespoke implementation code into repeatable, platform-ready tooling.
- Scope data requirements needed to support new product capabilities.
- Contribute to internal documentation, implementation playbooks, and tooling.
- Improve the efficiency and consistency of future customer implementations.
- Identify manual processes that can be automated and proactively develop solutions to improve operational efficiency.
- Contribute to the continuous improvement of data engineering and implementation practices.
Required Qualifications
- 4+ years of professional experience in data engineering, analytics engineering, technical consulting, or a related role combining hands-on data work with customer or stakeholder engagement.
- Advanced SQL skills with demonstrated experience analyzing, validating, and transforming complex or messy datasets.
- Proficiency in Python for data transformation, validation, and scripting.
- Experience with dbt or similar ELT/ETL frameworks.
- Experience working with data pipelines and modern data engineering practices.
- Ability to lead customer-facing technical engagements and communicate data requirements clearly.
- Experience managing customer expectations and driving technical deliveries through completion.
- Experience with cloud-based data infrastructure and modern development practices.
- Familiarity with Git, command-line environments, and collaborative codebases.
- Demonstrated ability to build internal tools or automate manual processes to improve operational efficiency.
- Experience leveraging AI-assisted development tools is beneficial.
- Comfort working with ambiguity and varied customer data environments.
- Proactive and self-directed approach with the ability to identify priorities and drive work through completion without close supervision.
Technical Environment
- Databricks
- dbt
- Dagster
- AWS
- SQL
- Python
- Git
- Command-line development environments
- AI-assisted development tooling
- Modern ELT/ETL and data pipeline technologies
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