Product Data Analyst
Senra Systems
| Company | Senra Systems |
| Category | Data & Analytics |
| Location | Redondo Beach |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 22 Jul 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (greenhouse) |
Description
At Senra Systems, we believe that the future of manufacturing lies not in automation, but in configuration.
We supercharge electrical wire harness manufacturing through software configuration, transforming skilled assembly tasks into high-throughput production lines. We enable customers to design what they want, when they want it and deliver harness builds at record-breaking speed.
We're looking for a Product Data Analyst to turn Senra's data into insight that improves both the software we build and the factory we run. This role has two sides:
• Product analytics — how people actually use Amp, our custom ERP/MES platform. You'll dig into usage data to see which features get adopted, where users get stuck or drop off, and whether what we ship actually makes people faster — feeding that straight back into the product roadmap.
• Business & operations metrics — the operational data captured in Amp (throughput, quality, cycle time, on-time delivery, cost) plus our other systems of record (QuickBooks for finance, Rippling for HR/timekeeping). You'll build the metrics and dashboards that show the business where we're strong and where we need to improve.
You'll report to the Product team and work across Product, Operations, and company leadership —one week measuring feature adoption, the next unpacking why a work center is running behind, the next preparing the numbers that go into a board deck.
This is a hands-on role, and it starts with the business — not the data. You'll first work to understand how Senra actually operates: what each team is trying to accomplish and where the friction and risk sit. From that understanding, you'll bring your technical skills to bear — querying, modeling, and visualizing the data to measure how we're doing and pinpoint where we have problems. Staying close to the physical processes behind the numbers, and to the operators, engineers, and managers who live them, keeps the metrics you build grounded in reality and focused on what the business actually cares about. The result: dashboards and metrics that operators, managers, and leadership rely on to act.
What you'll own
• Product usage analytics — analyzing how users move through Amp: feature adoption, workflow completion, funnels and drop-off, and the impact of new releases. Turning that into a clear signal for what Product should build or fix next.
• Operational & business metrics — building the core dashboards for throughput, quality, cycle time, on-time delivery, and cost, from raw data to a dashboard people check every day.
• Leadership & board reporting — owning the top-line metrics leadership runs on: the numbers that go into investor updates, board decks, and quarterly OKR tracking — accurate, consistent, and defensible.
• Digging into ambiguous questions on either side and turning them into trustworthy analyses — defining the metric, validating it against reality, and accounting for the data quirks and edge cases that would otherwise mislead.
• Partnering with Product to measure whether what we ship works — establishing baselines, tracking after launch, and reporting the business impact of the visibility you create.
• Prototyping metrics and dashboards yourself, proving them out with real users, then working with Product and engineering to codify the ones that stick into Amp and our reporting platform.• Spotting where the data we need doesn't yet exist — product instrumentation and operational capture — and advocating to close those gaps.
• Partnering across sales, quoting, order management, manufacturing, production, quality, supply chain, logistics, and engineering to surface the metrics each team needs.
• Data quality issues
• Operational trend charts about how the company is performing over time and where we
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