Sr. Data Engineer

SS&C Technologies•San Francisco, CA

10d•$120,000 - $160,000•Hybrid

About The Position

We’re looking for a talented Sr. Data Engineer to join our new Data Platform product team. In this role, you will work closely with our Product, Engineering, Cloud, and Service teams to build best in class infrastructure supporting data across the Eclipse and the Data Platform. This is a strategic, high-impact role that will also help shape the future of SS&C Eze products and services.

Requirements

Demonstrated experience shipping an LLM-backed feature to real users, and a clear account of how you measured its quality
Working knowledge of retrieval: embeddings, vector stores, and why naive RAG usually underperforms
Data Expertise: Proficient in data modeling, analysis, and relational database design. Financial setting experience is preferred.
Large Datasets: Skilled in processing and extracting value from large, disconnected datasets.
Data Pipelines: Experienced in designing and optimizing data pipelines and architectures.
Python Proficiency: Expertise in building data ingestion tools using Python, including web scraping and external APIs, and proficient in Python data science packages (numpy, pandas, scikit-learn, nltk, TensorFlow, matplotlib, etc.).
Machine Learning: Proficient in Machine Learning, NLP, or deep learning using Python.
PySpark: Experienced in working with PySpark.
ETL/ELT: Experienced in ELT/ETL development and handling unstructured datasets.
Data Warehousing: Knowledgeable in data warehousing and multidimensional data models.
Rapid Prototyping: Experienced in rapid prototyping and fast solution delivery processes.
Analytics: Skilled in working with large datasets to generate complex analytics for actionable insights.
Collaboration: Effective in collaborating across disciplines, departments, and segments.
Documentation: Experienced in developing and maintaining ETL-related artifacts such as schemas, data dictionaries, and transforms.

Nice To Haves

ETL/ELT Development: Proficient in ELT/ETL development.
Machine Learning: Experienced in Machine Learning, NLP, or deep learning using Python.
Unstructured Data: Skilled in handling unstructured datasets.

Responsibilities

Design and ship LLM-powered product features end to end, including retrieval-augmented generation and multi-step agent workflows
Build the evaluation harness before the feature — define what "good" means, measure it offline and in production, and catch regressions before clients do
Own prompt and context engineering as an engineering discipline: versioned, tested, and iterated against data rather than intuition
Build and tune retrieval infrastructure — chunking, embeddings, vector search, hybrid filtering — over structured and unstructured client data
Make it production-grade: latency budgets, cost per request, caching, fallbacks, and guardrails for unsafe or low-confidence output
Design, build, and own the core data models and key infrastructure to manage data and usage across the Eclipse Platform and Data Marketplace
Design and implement a data quality monitoring system. The system should include source-to-target data validations as well as anomaly detection
Collaborate closely with Engineering, Product Management, & IT, to inform product decision making with data and to identify opportunities for system improvements. Including recommendations and innovation for Information architecture and insights that are valuable to our customers
Collaborate with the Engineering, IT, and Security team(s) to meet data governance requirements
Build dashboards to help Leadership monitor performance, availability, and user behavior of the platform
Answer questions from the leadership team for reporting, publications, and industry reports
Think creatively to find optimal solutions to our complex, often unstructured problems