Senior MS SQL Engineer

#HIRING at Plurio by Elly Analytics

Location: Remote — Americas (US) timezone Reports to: Head of Engineering Type: Full-time Created: April 2026

🚀 About Plurio

Plurio is an AI agent for performance marketing teams. We're not another dashboard or analytics tool — we're building AI that actually does the work: analyzes campaigns, spots problems, reallocates budgets, and explains what's happening in plain language.

We've raised $4.5M total, with the most recent $3.5M announced in March 2026. We work with marketing teams spending $100K–$5M/month on paid ads (Meta, Google, TikTok) — companies with complex funnels, long conversion chains, subscription and repeat-purchase models, and LTV-driven optimization (think EdTech, FinTech, Consumer Apps, Healthcare, Home Services).

The team is 30+ people, fully remote, and radically AI-first — everyone works in Cursor with a shared workspace. We don't just sell AI; we run on it.

🚀 Why This Role Exists

Plurio’s attribution engine is a core part of the product. It processes complex customer journeys and determines how marketing spend translates into real business outcomes.

It already works in production and supports real clients, but it has grown into a system with complex logic, heavy queries, and non-trivial performance trade-offs. As we scale, these challenges become more visible and more important to solve.

We are looking for someone to take ownership of how this engine evolves. This includes improving performance, simplifying where possible, and making the system easier to reason about, while preserving the flexibility our clients rely on.

This is a hands-on role at the intersection of data modeling, SQL performance, and real-world business logic. You will be working on a system where changes are not isolated, and every decision has real impact.

🎯 What You'll Own

Attribution engine evolution Lead the technical side of rebuilding the attribution algorithm and related logic while keeping client-facing flexibility — schema, batch logic, performance, and safe rollout.

SQL Server as a product Own T-SQL, schema, and indexing for analytical and transactional workloads; read query plans, find regressions, and tune with measurable before/after.

Performance and safe change Lead performance tuning (indexes, statistics, partitioning where it fits), capacity thinking, and careful rollout of DB changes in live systems — using SQL Profiler, actual execution plans (including dynamic SQL), and columnstore where they apply.

ETL and data paths Improve ETL and data pipelines that feed downstream analytics — including data consumed by AI-driven reporting and agent workflows — without making this an “AI product engineer” job title.

Collaboration with engineering Partner on data model and service boundaries: migrations, backward compatibility, clear contracts between app and database.

C# at mid level Ship and review C# in ASP.NET Core (or similar) — async paths, data access through any ORM you have used in production, tests — where the work sits between API and database. Deep staff-level C# architecture is not the primary expectation.

Quality and operations Participate in code review, incident analysis, and technical discussion; use Git and CI/CD the way the team does. AI-assisted coding is part of how we work — we actively use various AI tools (Cursor, Copilot-class tools, and more) and provide team access to premium services; use them where they improve quality and speed, not as a substitute for judgment.

🔥 You're the Right Person If

Requirements: — A minimum of five years of hands-on experience in MS SQL is required. — A minimum of two years of hands-on experience in C# development is required. — Solid knowledge and application of SQL Profiler, reading dynamic query plans, and columnstore. — Experience and interest in AI-assisted coding (we actively use various AI tools and provide team access to premium services). — Experience working with any ORM in C#. — Proficiency in using version control systems, particularly Git. — You live and work in an Americas (US) timezone with reliable overlap on US business hours — required.

⚡ It'd Be a Plus If

• Advanced experience with MS SQL performance tuning, NoSQL databases, or building dbt pipelines.
• Familiarity with marketing attribution concepts — helpful context; algorithm and SQL skills come first.
• Experience with distributed / clustered SQL Server, Always On, HA patterns, or scale-out / operations beyond a single instance.
• Extended Events, RabbitMQ, Docker, observability (Grafana, Prometheus), or Azure DevOps in production.
• Public SQL or C# samples on GitHub/GitLab we can read.

🚀 What You Can Expect

• High pace, real systems: You'll work on production paths that matter to clients and the team — iteration, ownership, and clear technical standards.
• Direct collaboration with engineering and analytics-adjacent roles; your work is visible in data quality, cost, and attribution correctness.
• Competitive compensation — aligned with senior data/SQL experience; specifics discussed individually (we don't publish a fixed band in the posting).
• Remote, Americas timezone: you must be in an Americas (US) timezone and work US business hours. Nearly all clients and core team collaboration run on US time — timezone overlap is a hard requirement.
• AI-first engineering culture: We use Cursor, Claude, shared AI infrastructure, and premium team access for coding assistance — alongside strong SQL and attribution craft.

🚀 Hiring Process

1. Application — CV + short answers. We aim to respond within 48 hours.
2. Intro call (People & Operations manager, ~30 min) — company overview, background, motivation, logistics.
3. Technical interview — deep dive on SQL Server (design, tuning, real scenarios, including dynamic query plans / dynamic SQL) and C# / ORM / data access where it meets the database.
4. Paid trial period — we always include a short paid collaboration (2–4 weeks, part-time or full-time) as part of the process. This is not a test task. You will work with the team on real problems, get familiar with the system, and see how we operate day-to-day. It is a two-way evaluation for both you and us.
5. Final conversation — we discuss outcomes of the trial, ways of working, and team fit.
6. Offer — if it is a fit, we align on terms and start date.