Your Team's Impact

This role sits within FactSet’s Cloud and Managed Services(part of Platforms and Environments department within Data Solutions) , which is responsible for delivering FactSet Bulk Data feeds to the desired Cloud Platform by Clients.

The C&MS Engineering team is responsible for the development of data pipelines that handle distribution of FactSet Content Data across various cloud platforms such as Snowflake, Databricks, AWS S3, Azure Blob and Amazon Redshift.

As part of a new initiative focused on delivering Vectorized Data as part of FactSet Intelligence Platform , the team is expanding its capabilities to support evolving business needs and the increasing demand for vectorized data. We will utilize native AI engines such as Cortex in Snowflake and Genie in Databricks to come up with product lines that help clients leverage the vectorized data into their agentic workflows.

We are looking for a Software Engineer III (Go and SQL) to join the C&MS team in Hyderabad.

You will contribute to building and evolving data delivery systems , with a strong focus on speed, delta processing, system design, and performance .

This role combines data engineering and backend development , with significant exposure to enterprise cloud platforms , large-scale datasets, SQL-heavy workflows, and system reliability challenges .

You will also contribute to the team’s ongoing transition from Perl-based systems to Python , helping modernise the technical stack.

Technical Environment : Go(primary), Python, SQL (high usage), REST APIs, Git, CI/CD pipelines

Cloud Platforms: Snowflake, Databricks, Amazon Redshift, Azure, AWS

The team: 5 software engineers based in India, 3 based in US and 2 in UK

What You'll Do

As an experienced Software Engineer with a background in data engineering, you will lead and deliver solutions that enable efficient vectorized data management and retrieval across our Snowflake and Databricks platforms. You will also mentor a growing team, collaborate with stakeholders and implement scalable data pipelines optimized for large-scale analytical and AI/ML workloads.

• Contribute end-to-end design, implementation, and optimization of data pipelines for vectorized data storage, transformation, and serving.

• Collaborate with cross-functional teams (data science, analytics, product) to define data requirements, architecture, and data models optimized for vector data.

• Deliver high-performance data solutions using Databricks (Spark, MLflow, Delta Lake) and Snowflake, leveraging vectorized storage and retrieval mechanisms.

• Develop robust ETL/ELT processes for large datasets, including data ingestion, feature extraction, and index building for vector similarity search.

• Enhance existing data platforms to support vector data workloads, integrating with LLM and other AI/ML services as needed.

• Ensure data governance, security, quality, and compliance with internal and external regulations.

• Monitor, troubleshoot, and optimize data pipelines and processes for cost and performance.

• Stay on top of emerging technologies in vector databases, cloud data platforms, and AI/ML infrastructure.

What We're Looking For

• 3+ years of experience in system design for large-scale, distributed systems

• Good proficiency in Python, C#, python, AWS, Snowflake/Databricks and/or Go preferred programming

• Experience with cloud-native application development and cloud platforms (AWS/Azure/GCP)

• Hands-on experience with vector data storage, vector databases, and similarity search (e.g., ANN, Faiss, Pinecone, Milvus).

• Hands-on expertise with modern datalake and cloud data platforms such as Snowflake, Databricks, Redshift, or similar

• Experience in batch and/or real-time data processing applications

• Experience integrating data platforms with AI/ML or LLM applications is a strong plus.

• Strong written and verbal communication skills

• Strong pragmatic, iterative approach to problem solving and prototyping

• Experience with DevOps tools, CI/CD pipelines, and modern software development best practices