Senior Data Engineer - Quant & AI Infrastructure

About The Role

Algocor is looking for a Senior Data Engineer to own the high-volume data ingestion and time-series data infrastructure behind our quant and AI systems.

We are building a trading system where research, execution, market data, and an LLM-based agent layer operate on the same infrastructure. For this system to work reliably, the data layer needs to be more than a pipeline. It needs to be well-structured, observable, recoverable, and trusted by both quant systems and AI agents.

What You’ll Own

You will design, build, and operate the data ingestion, transformation, storage, and access layer that connects external market-data providers, exchange and broker APIs, on-premise and cloud-based systems.

• Building and maintaining high-volume ETL/ELT pipelines from market-data providers such as Pyth, Databento, exchange APIs, and broker APIs into our on-prem stack


• Designing and operating streaming data pipelines from WebSocket and API sources


• Owning production-grade time-series database design and operations using systems such as QuestDB, kdb+, ClickHouse, TimescaleDB, or similar


• Designing data structures for tick data, OHLCV, symbols, derived signals, and internal datasets


• Making decisions around partitioning, retention, compression, schema evolution, query performance, and storage strategy


• Designing the boundary between on-prem systems and AWS-based cloud components


• Deciding what gets calculated where, how data is synchronized, and how sync health is monitored


• Handling streaming failure scenarios such as reconnect logic, replay, backfill, duplicates, out-of-order events, late-arriving data, and gap detection


• Writing production-grade Python services and APIs, including FastAPI, to expose clean and validated data to internal systems and the AI layer


• Owning validation rules, data quality checks, observability, alerting, and recovery procedures


• Building agent-facing data access tools such as query interfaces, document retrieval flows, and dataset endpoints


• Documenting datasets, schemas, access rules, operational assumptions, and failure modes so the rest of the team can build confidently on your work

This is a role where you will be expected to scope, build, ship, monitor, and improve the systems you own.

Why This Role Matters

Our AI layer is only as reliable as the data infrastructure underneath it.

In this role, your work will directly shape how confidently we can use data across quant research, execution systems, internal tools, and AI agents.

You will be close to the architecture, the data, and the people building on top of it. This is a high-ownership role in a small, focused team where individual contribution is visible.

You will have:

• End-to-end ownership of the data ingestion and storage layer


• Direct collaboration with the Engineering and quant team on architecture


• A modern stack with real engineering problems


• The opportunity to build infrastructure that directly supports quant and AI systems

What We’re Looking For

We are looking for a senior engineer who can make independent decisions around data ingestion, storage, streaming reliability, and production data infrastructure. You are likely to be a strong fit if you have:

Production Data Engineering Experience

You have built or operated data systems in production, not just experimental projects, dashboards, or offline analytics pipelines.

High-volume Ingestion Experience

You understand what changes when data volume grows significantly - including throughput, batching, partitioning, storage cost, write performance, backfill strategy, and operational monitoring.

Streaming pipeline ownership

You have worked with continuously flowing data from APIs, WebSockets, message brokers, or event streams. You understand replay, gap detection, ordering, duplicates, late data, and recovery.

Time-series Database Experience

You have hands‑on experience with time-series or high-volume analytical databases such as QuestDB, kdb+, ClickHouse, TimescaleDB, or similar systems. You understand data modeling, partitioning, retention, query performance, and operational trade-offs.

Strong Python and SQL fundamentals

You write production‑grade Python and strong SQL. You can build maintainable pipelines, services, and APIs that other systems depend on.

Production ownership mindset

You are comfortable with Docker, Linux, Git, CI/CD, monitoring, alerting, incident response, documentation, and owning what happens after something is shipped.

Clear technical communication

You can take a research, business, or product need and turn it into a practical technical specification without overcomplicating the process.

Location

Büdotek Teknopark, Istanbul