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. Because this field is still developing in Azerbaijan, this role offers a unique opportunity for someone who wants to work at the intersection of large-scale data engineering, financial markets, and quantitative research. The person we are looking for sits between engineering and research: someone with solid software and data engineering skills, enough data science and machine learning to work alongside our researchers, and the curiosity to dig into the data on their own. About the Role: 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. Performance is one of the core engineering challenges here. We work with years of minute-level market data across multiple instruments, and research means running the same computation thousands of times over that data. Code that takes hours instead of minutes directly limits how much research the team can do. We need someone who can identify performance bottlenecks and propose solutions that make the code efficient. This role is more than classic data engineering. Roughly half of it is engineering; the other half is research. Because the team is small, the same person follows the whole path: raw exchange data, storage and pipelines, the fast computations that run on top of them, the datasets and features that feed our backtests and machine learning models, and the analysis that comes out at the end. We want someone who experiments with the data, asks their own questions, finds patterns nobody thought to look for, and comes back with proposals instead of waiting for tickets. You will work day to day with our quantitative researchers, and modern AI tools are a normal part of how we all work. If you enjoy the moment when a dataset finally tells you something surprising, this role is for you. You will help us answer questions such as: How do we process years of market data in minutes instead of hours, and where is the bottleneck: I/O, memory, or computation? How should this data be stored, partitioned, and typed so that thousands of backtests can run over it efficiently? Is this dataset actually correct? Are there gaps, duplicates, misaligned timestamps, or silently wrong values? Can this computation be vectorized or compiled, or does it need a different algorithm and data structure altogether? What features can we derive from raw market data to feed a machine learning model, without leaking future information into the past? Is this pattern real, or does it only look real because we tested a thousand variations and kept the best one? What is in this data that nobody has looked at yet? Job Responsibilities: Design, build, and maintain 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 through 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, look for patterns, and bring hypotheses and proposals to the research team Write clean, tested, reproducible Python following solid engineering practices (modular design, version control, code review, documentation) Design SQL 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 What we're looking for: Bachelor’s degree in computer science, engineering, mathematics, statistics, data science, or a related quantitative discipline At least 2 years of professional experience in data engineering, software engineering, data science, or a similar field Strong analytical thinking and genuine enjoyment of mathematical methods, exploring data, and doing research (the single most important criterion for us) 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 fit comfortably in memory Practical understanding of performance: profiling code, identifying whether the bottleneck is CPU, memory, or I/O, and fixing it Strong Python skills with 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 hypothesis testing basics Machine learning knowledge: classic algorithms (linear and logistic regression, decision trees, random forests, gradient boosting, clustering), when and why to use each, cross-validation, and pitfalls such as overfitting and data leakage Daily use of AI-assisted coding and research tools, with the habit of verifying what they produce Ability to work independently and carry a problem from a vague question to a finished, verified result Initiative and research ability: investigating unfamiliar problems, forming your own opinion, and proposing ideas Deeper statistical methods (multiple-testing control, bootstrap, time-series statistics) and ML on noisy, low signal-to-noise data are a strong plus Exposure to financial markets or crypto data, workflow tools (Airflow, Prefect, Dagster), and awareness of look-ahead and survivorship bias are a plus Why join us? 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. Working hours: 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. Salary: Competitive and negotiable based on experience and knowledge. We look forward to receiving your application. Please be advised that only shortlisted candidates will be invited to the further stages of the recruitment process. Interested candidates can send their CV to the e-mail address in theApply for jobbutton.