AI Data Engineer

Job details

Work model

Not provided by source

Salary

Conflicting source ranges

Location

New York, New York, United States

Hiring context

How this role compares at Schonfeld

Schonfeld has 67 live roles in MeritLog’s catalog across 8 job families, and 38 of them are in data & analytics. 12 of those listings publish a pay range, a disclosure rate of 18%.

Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.

What the role asks for

Parsed by MeritLog from the employer’s own posting. The full description follows below.

Job description

About the Role Schonfeld Strategic Advisors is seeking an experienced AI Data Engineer to join our Data Engineering team. In this role, you will be responsible for designing, building, and maintaining robust data pipelines that power SchonAI, our firm's internal AI platform. You will work at the intersection of data engineering and AI, ensuring that high-quality, timely, and relevant data flows seamlessly to our AI systems to support investment professionals across the firm. Key Responsibilities Data Pipeline Development • Design and build scalable, reliable data pipelines to ingest, transform, and deliver structured and unstructured data to SchonAI using Prefect. • Develop ETL/ELT processes for diverse data sources including market data, research documents, internal databases, and third-party APIs. • Implement real-time and batch data processing workflows to meet varying latency requirements. • Ensure data quality, consistency, and integrity across all pipelines. AI Data Infrastructure • Build and maintain data infrastructure optimized for AI/ML workloads, including vector databases and semantic search systems. • Design data schemas and storage solutions that support efficient retrieval and processing for LLM applications. • Implement data versioning, lineage tracking, and observability for AI training and inference pipelines. • Optimize data delivery for low-latency AI interactions and high-throughput batch processing. Integration & Collaboration • Partner with AI engineers, software developers, and data scientists to understand data requirements. • Integrate with existing firm systems including risk platforms, trading systems, portfolio management tools, and research databases. • Collaborate with infrastructure teams on cloud architecture, security, and compliance requirements. • Work closely with business stakeholders to prioritize data sources and pipeline enhancements. Data Governance & Security • Implement appropriate data access controls, encryption, and compliance measures. • Ensure adherence to data governance policies and regulatory requirements. • Monitor and maintain data pipeline performance, reliability, and cost efficiency. • Document data flows, transformations, and dependencies. Required Qualifications Technical Skills • Programming: Strong proficiency in Python; experience with SQL and at least one other language (e.g. Java, Scala, Go, Rust) • Data Engineering: 5+ years of experience building production data pipelines using tools like Apache Airflow, Prefect, Dagster, or similar • Big Data Technologies: Hands-on experience with distributed computing frameworks (Spark, Flink) and modern data platforms • Cloud Platforms: Proficiency with AWS services (S3, Kubernetes) or equivalent GCP services • Databases: Experience with both SQL (PostgreSQL, MySQL) and NoSQL databases (MongoDB, DynamoDB, Elasticsearch) • AI/ML Data: Understanding of data requirements for ML/AI systems, including experience with vector databases (Pinecone, Weaviate, Qdrant) and embedding pipelines Preferred Experience • Experience building data pipelines for LLM applications or RAG (Retrieval Augmented Generation) systems • Familiarity with financial data sources (market data, fundamental data, alternative data) • Knowledge of data streaming technologies (Kafka, Kinesis, Pub/Sub) • Experience of Analytics/Warehouse/OLAP DB (BigQ, SingleStore, RedShift, ClickHouse) • Experience with containerization (Docker) and orchestration (Kubernetes) • Understanding of MLOps practices and tools • Experience with data quality frameworks (Great Expectations, Deequ) Professional Skills • Bachelor's or Master's degree in Computer Science, Data Engineering, or related technical field • Strong problem-solving skills and attention to detail • Excellent communication skills with ability to translate technical concepts for non-technical stakeholders • Experience working in fast-paced, collaborative environments • Self-motivated with ability to manage multiple priorities Who we are Schonfeld is a global multi-manager hedge fund that strives to deliver industry-leading risk-adjusted returns for our investors. We leverage both internal and external portfolio manager teams around the world, seeking to capitalize on inefficiencies and opportunities within the markets. We draw from decades of experience and a significant investment in proprietary technology, infrastructure and risk analytics to invest across four main strategies: Quant, Tactical, Fundamental Equity and Discretionary Macro & Fixed Income. Our Culture At Schonfeld, we’ll invest in you. Attracting and retaining top talent is at the heart of what we do, because we believe that exceptional outcomes begin with exceptional people. We foster a culture where talent is empowered to continually learn, innovate and pursue ambitious goals. We are teamwork-oriented, collaborative and encourage ideas-at all levels-to be shared. As an organization committed to investing in our people, we provide learning and educational offerings and opportunities to make an impact. We encourage community through internal networks, external partnerships and service initiatives that promote inclusion and purpose beyond the firm’s walls. The base pay for this role is expected to be between $225k and $275k. The expected base pay range is based on information at the time this post was generated. This role may also be eligible for other forms of compensation such as a performance bonus and a competitive benefits package. Actual compensation for the successful candidate will be determined based on a variety of factors such as skills, qualifications, and experience. #LI-PW1

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