Data Engineer

Fasanara Digital was established 8 years ago and is the crypto arm of Fasanara Capital, a 15-year-old boutique alternative asset manager. We are a Quantitative Investment fund applying a scientific approach to investing in crypto assets. Our goal is to achieve exceptional risk‑adjusted returns. We pursue a range of diversified and highly sophisticated investment strategies that seek to profit from inefficiencies in the market structure and range from market making to cross‑exchanges arbitrage. We are strong believers in meritocracy, and we reward people based on impact and excellence. There is no bureaucracy of large organisations; the environment is collaborative, entrepreneurial, and is based on trust. We set ambitious goals, work hard, stress teamwork, and adhere to the highest level of excellence in everything we do. We are only as good as our team. Thus, we are building the firm around exceptional talent.

As a Data Engineer at Fasanara Digital, you will be responsible for the design, development, and maintenance of real‑time and historical market data pipelines that underpin our alpha research programmes and live mid‑frequency trading strategies. You will work closely with quantitative researchers and trading engineers to ensure that high‑quality, low‑latency data is available at every stage of the research and production lifecycle. You will also take ownership of evaluating and adopting best‑in‑class technologies for ingesting, storing, and serving large volumes of market data for back‑testing and strategy development.

Key Responsibilities

• Design, build, and maintain robust real‑time market data pipelines from multiple cryptocurrency exchanges and data vendors, ensuring high availability, low latency, and data integrity across all feeds.
• Manage and optimise historical data storage infrastructure, including the ingestion, normalisation, and cataloguing of tick data, order book snapshots, trade data, and derived market signals for use in quantitative research and back‑testing.
• Collaborate closely with quantitative researchers and trading engineers to understand data requirements, delivering clean and well‑documented datasets that accelerate alpha discovery and strategy development.
• Build and maintain efficient data‑serving layers (e.g. time‑series databases, data APIs, query engines) capable of handling large‑scale historical queries and real‑time data access patterns required by research and live trading systems.
• Monitor pipeline health, implement alerting and observability, and respond rapidly to data quality issues, feed outages, or latency degradation that could impact live trading performance.
• Evaluate and introduce modern data engineering technologies and best practices — including streaming frameworks, columnar storage formats, distributed query engines, and cloud‑native data platforms — to continually raise the bar on throughput, reliability, and researcher productivity.
• Document data schemas, pipeline architectures, and operational runbooks to ensure knowledge is shared across the team and systems remain maintainable at scale.

Requirements

• Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, Mathematics, or a related quantitative or technical field.
• Minimum of 3 years of professional experience in data engineering, with a strong track record of building and operating production‑grade data pipelines in a high‑throughput or time‑sensitive environment.
• Proficiency in Python and/or a JVM‑based language (Java/Scala) for pipeline development, with solid SQL skills for querying and transforming large datasets.
• Hands‑on experience with real‑time streaming technologies such as Kafka, Redpanda, or similar, and familiarity with both batch and micro‑batch processing frameworks.
• Experience with time‑series or columnar databases (e.g. TimescaleDB, InfluxDB, Arctic, ClickHouse, kdb+, or Parquet‑based data lakes), and an understanding of the trade‑offs between storage and query performance at scale.
• Familiarity with cloud infrastructure (AWS, GCP, or Azure) and infrastructure‑as‑code practices; experience with containerised and orchestrated workloads (Docker, Kubernetes) is a plus.
• Experience in financial services, quantitative trading, or a data‑intensive technology company is strongly preferred; direct exposure to cryptocurrency or digital asset markets is a plus but not required.

Benefits

• Competitive bonus scheme.
• Bupa health & dental, Cycle to Work scheme, enhanced pension, and generous annual leave.
• Enhanced parental leave, special leave allowances, and charity giving options.
• Regular team events, legendary summer & Christmas parties, knowledge sharing sessions, and quarterly town halls.
• Team lunches, dinners, Friday drinks, team sport activities.