Gigawatt AI

Gigawatt AI

Integration Consultant, Tech

Remote (United States)

Sponsorship not specifiedDetected 2 days ago
ReactSQLGraphQLRESTKafkaData EngineeringOutbound SalesProcurementCommunication

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Cap-exempt (no lottery)0
Sponsors this role0
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70

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About the role

  • Gigawatt's platform is only as valuable as the data feeding it.
  • Before a customer can go live, their source-system data - CIS and billing, meter and interval usage, outage, clearinghouse, and more - has to be mapped, migrated, loaded, validated, and then kept in sync as the platform runs.
  • This role is the hands-on technical person who makes that happen.

Responsibilities

  • Own customer data readiness - Profile source systems, define the data each module needs, secure access, and get that data into shape to load - surfacing quality and structure issues early.
  • Build and run migration and conversion loads - Do the source-to-target mapping, run mock and iterative loads with the data team, validate results, and reconcile against the legacy system to stand up each go-live and work through the cleanup that follows.
  • Build inbound integrations - Stand up the four-pattern feeds (API read/write, Kafka initial load, Kafka CDC) through GoldenGate, Confluent, and Boomi into the Gigawatt replica.
  • Build outbound flows - Build the flows that publish Gigawatt data, bills, events, and results back out - for example bill results into the CIS / MACSS, EDI and market transactions, and curated data or regulatory outputs.
  • Own data mapping and quality, both ways - Maintain source-to-target and target-to-source mappings and a validation approach so data lands complete and correct in either direction and gaps are caught before testing.
  • Handle scale and performance - Build loads and syncs that perform against large data volumes and high-row-count tables, and tune them when they don't.
  • Reuse and contribute to the framework - Build on the shared patterns the Integrations Director sets, and feed reusable connectors and improvements back so each new connection is faster than the last.
  • Support cross-module delivery - Deliver the integration and data work the Customer, Revenue, and Service teams depend on, on the sequence the Director sets.
  • What You'll Bring Hands-on integration and data engineering - Real experience designing and building enterprise integrations and data flows that you build yourself - REST / GraphQL APIs, webhooks and events, batch, change-data-capture, and ETL - in code and configuration, not just specify for others.
  • Data migration and conversion - A track record of source-to-target mapping, mock and production loads, validation, reconciliation, and cutover support on real implementations.

Nice to have

  • Exposure to specific utility systems - Oracle CC&B / CCS, MV90, MDM / head-end, OMS, clearinghouse - and to EDI and retail-choice / market flows (enrollment, load settlement).
  • Experience with enterprise data warehouses (e.g., EDW) and large-scale data loading and synchronization.
  • Experience with event-driven architecture and webhook / event platforms.
  • How We'll Measure Success Customer data lands complete, correct, and on time for each go-live, with migration and conversion loads reconciled and cleanup closed out.
  • Your data mappings land complete and correct in both directions, with gaps caught before testing.
  • You contribute reusable connectors and improvements back into the framework, and the module teams that depend on your work stay unblocked.

Benefits

  • Competitive salary and equity package.
  • Comprehensive benefits including health insurance, remote-work flexibility, and 401(k) match.

Company info

  • We are live with our first major utility customer and scaling fast.
  • The work is cross-module - Customer, Revenue, and Service - with our anchor customer as the proving ground where the approach is hardened and then reused for the customers we sign next.
  • This is the kind of hands-on solutions and data-integration work we are already doing today; we are adding capacity to do more of it, reliably and at scale.
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This listing is sourced directly from Gigawatt AI's careers page and normalized into a canonical job model.