Data engineer

We are a data-driven algorithmic trading firm building systematic trading strategies for financial markets. Our work combines financial market research, data analysis, backtesting, and practical strategy development. We are looking for a Data Engineer to own the data layer behind our research and backtesting: collecting, validating, storing, and processing large volumes of market data, and making sure that everything built on top of it runs correctly and quickly.

• Design, build, and maintain the data pipelines that collect, clean, validate, and store large volumes of market data.
• Own data quality: gap detection, deduplication, timestamp integrity, reconciliation between sources, and automated validation.
• Profile and optimize performance-critical code: vectorization, memory layout, chunking, JIT compilation, parallelism, and efficient storage formats.
• Build and maintain the datasets and feature sets used for backtesting and machine learning.
• Support machine learning work end to end: feature engineering, dataset preparation, training and evaluation pipelines, and protection against data leakage.
• Analyze data independently, apply statistical and mathematical methods to it, look for patterns, and bring hypotheses and proposals to the research team.
• Write clean, tested, reproducible Python and apply solid engineering practices: modular design, version control, code review, and documentation.
• Work with SQL databases: design schemas, write complex queries, and optimize them.
• Research new tools, libraries, and approaches, evaluate them honestly, and propose the ones worth adopting.
• Communicate findings clearly to both technical and non-technical team members.

• Bachelor's degree in computer science, engineering, mathematics, statistics, data science, or a related quantitative discipline.
• At least 2 years of relevant professional experience in data engineering, software engineering, data science, or a similar field.
• Solid software engineering fundamentals: data structures, algorithms, complexity analysis, clean and modular code, testing, and version control.
• Hands-on experience with large datasets (millions of rows and beyond), including data that does not comfortably fit in memory.
• Deep, practical understanding of performance: the ability to profile code, identify whether the bottleneck is CPU, memory, or I/O, and fix it. Vectorization, batching, parallelism, and efficient data formats should be working tools for you, not theory.
• Strong Python skills and command of the data processing stack: pandas, NumPy, and numba.
• Strong SQL skills: complex joins, aggregations, window functions, indexing, and query optimization.
• Comfortable with Linux, Docker, cloud infrastructure, and CI/CD.
• Basic knowledge of statistics: descriptive statistics, probability, distributions, variance, correlation, sampling, and the basics of hypothesis testing.
• Machine learning knowledge: enough to support our researchers properly. You know the classic algorithms (linear and logistic regression, decision trees, random forests, gradient boosting, clustering) and, more importantly, when and why each one is the right choice. You are aware of the core best practices (train/validation/test splitting and cross-validation) and of the classic pitfalls, such as overfitting and data leakage.
• AI-assisted coding: LLM-based coding and research assistants are already part of your daily workflow. You use them to move faster, and you verify what they produce instead of trusting it.
• Strong analytical thinking and genuine enjoyment of working with mathematical methods, exploring data, and doing research. This is the single most important criterion for us.
• Ability to work independently and take ownership: carrying a problem from a vague question to a finished, verified result without close supervision.
• Initiative and research ability: investigating unfamiliar problems, forming your own opinion, and proposing ideas rather than waiting to be told what to do.
• Будет плюсом: Deeper statistical methods: hypothesis testing on real data, multiple-testing and false-discovery control, bootstrap and resampling methods, and time-series statistics; Experience applying machine learning to noisy, low signal-to-noise data; Basic understanding of financial markets, trading concepts, and trading data, and any hands-on exposure to market or crypto data; Experience with orchestration or workflow tools (Airflow, Prefect, Dagster), useful, but not expected; Awareness of data pitfalls in research settings: look-ahead bias, data leakage, survivorship bias, and unrealistic assumptions

• Work at one of the first corporate-level algorithmic trading companies in Azerbaijan.
• Own the data layer of a real research and trading system from the ground up.
• Solve genuine performance problems at scale, where your optimizations are measured in hours saved every day.
• Have direct influence on research direction: your findings and proposals shape what we test next.
• Work closely with quantitative researchers and strategy developers in a small, fast-moving team within a fast-growing company.
• Grow professionally, with real opportunities for development and career progression.
• Private health insurance.
• Lunch, drinks, snacks, and fruit provided by the company.
• A centrally located office with modern amenities, including a game room and nap spaces.
• You’ll be working 5 days a week from 10 a.m. to 7 p.m. (13:00-14:00 lunch time). Four days will be based in the office, and one day (Friday) you can work from home