Senior Data Engineer

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🕒 March 24

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Arcana Analytics

WebsiteLinkedInAll Job Openings

1 - 10 employees

Founded 2020

💸 Finance

☁️ SaaS

🤝 B2B

💰 Funding Round - Arcana on 2022-04

Finance • SaaS • B2B

Arcana Analytics is a B2B SaaS company that provides advanced fundamental and statistical equity factor risk models, proprietary crowding and ownership datasets, and analytics tools for institutional investors. Its platform offers portfolio-level and single-stock factor/idiosyncratic decomposition, scenario analysis, optimization, live intraday factor returns, and APIs to support portfolio construction, risk management, and idea/hedge screening. Arcana serves hedge funds and asset managers seeking to improve risk-adjusted returns and understand crowding, positioning, and drivers of performance.

📋 Description

• Develop and implement real-time data ingestion and processing systems. • Design, build, and operationalize large-scale enterprise data solutions and applications. • Create and manage production data pipelines, from ingestion to consumption, within a big data architecture using PySpark. • Leverage expertise in Python, Airflow, SQL, and cloud platforms to build robust solutions (strong knowledge of these is essential). • Translate complex business challenges into scalable and efficient technical solutions. • Work collaboratively with a high-performing data engineering team, owning the full lifecycle of solution implementation.

🎯 Requirements

• Bachelor's Degree in Computer Science, Information Technology, or a similar discipline. • 3 - 8 years of professional experience in data engineering or related fields. • Proven experience with ETL processes, data integration, and handling large-scale datasets using PySpark. • Strong understanding of data engineering concepts like ETL, near/real-time streaming, data structures, and workflow management. • Experience with version control tools like Git/GitHub. • Proficiency in SQL and Data Warehouse concepts. • Experience working with SQL or NoSQL databases like Cassandra, MongoDB, or HBase. • Knowledge of AWS technologies such as EMR, RedShift, Kinesis, Lambda, Glue, S3 IAM, CloudWatch, and big data tools like Hadoop/EMR, Hive, and Sqoop.