> Markdown version of [/jobs/ext/3951012-senior-software-engineer](https://www.wearedevelopers.com/jobs/ext/3951012-senior-software-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Senior Software Engineer

- **Company:** Franklin Resources,Inc.
- **Location:** Baltimore, MD, United States
- **Experience:** Expert
- **Salary:** $122,000.0 - $158,300.0
- **Contract:** Permanent contract
- **Skills:** Query Performance, Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Application Frameworks, ARM Architecture, JIRA, Microsoft Azure, Cloud Computing, Software Quality, Code Review, Continuous Integration, Data Governance, Data Warehousing, Dimensional Modeling, Python (Programming Language), Liquibase, Performance Tuning, Query Optimization, Role-Based Access Control, Cloud Services, Microsoft Copilot, Standard Sql, Software Engineering, SQL Stored Procedures, SQL Databases, Systems Integration, Data Logging, Data Ingestion, Azure Data Factory, GitHub Copilot, Snowflake, Concurrency, Gitlab, Build Management, Star Schema, Data Management, Pagination, Data Pipelines, Jenkins
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-software-engineer-baltimore-md--1d58a385-ec3d-41f1-b147-1638e247dfe2

## About the Role

Software Engineering, Customer Relations, Asset Management, Snowflake Schema, Cost Control, Performance Tuning/Optimization, Data Quality, Data Analysis, Service Level Agreement (SLA), Data Management, * Strong hands-on experience with Snowflake, including:

 * Solid understanding of Snowflake architecture (storage, compute, cloud services)
 * Virtual warehouses, workload isolation, and concurrency handling
 * Strong query performance tuning and cost optimization
 * Stored Procedures, Tasks, and orchestration patterns
 * Exposure to or experience with Dynamic Tables
 * Strong expertise in:
 * ELT frameworks (incremental processing, CDC, auditing, error handling)
 * Data warehousing and dimensional modeling (Facts, SCD Type 1/2)
 * Strong SQL and query optimization skills
 * Python for orchestration and integrations
 * Experience building and operating production-grade data pipelines
 * Familiarity with Medallion Architecture (Bronze/Silver/Gold)

Nice to have skills

 * Advanced clustering and performance optimization
 * Data governance (RBAC, masking)
 * Experience with ADF, APIs, Blob storage
 * CI/CD tools (GitLab, Jenkins, Liquibase)
 * Exposure to Snowflake Cortex / AI capabilities
 * GitHub Copilot and AI-assisted development tools

## Description

* Design, build, and optimize Snowflake-based data platforms and ELT pipelines
 * Own pipeline reliability, performance, and cost optimization
 * Deliver scalable, high-quality data for analytics and business decision-making
 * Ensure SLAs and reliability for production pipelines
 * Implement solutions aligned to defined architecture and engineering standards

How you will add value

 * Design and build end-to-end data pipelines: Source Raw Standard Consumption
 * Implement robust incremental frameworks (append-only, CDC, watermark-based)
 * Develop ELT pipelines using Snowflake SQL, Stored Procedures, Tasks, and orchestration patterns
 * Design and manage ingestion pipelines (API ADF Blob Snowflake), handling real-world challenges (pagination, retries, schema changes)
 * Build and optimize complex transformations within Snowflake
 * Design and maintain dimensional models (fact tables, SCD Type 1/2)
 * Implement data quality, monitoring, logging, and alerting mechanisms
 * Drive query performance tuning (Query Profile, clustering, pruning)
 * Optimize warehouse usage, concurrency, and cost efficiency
 * Implement RBAC, masking, and governance standards
 * Lead code reviews, best practices, and reusable frameworks
 * Collaborate with architects, analysts, and business stakeholders to translate requirements into solutions
 * Ensure SLA adherence for production pipelines

AI Fluency - AI-first mindset

 * Uses AI tools (Copilot, Cortex) to accelerate development, troubleshoot pipelines, and improve code quality
 * Uses AI to assist in designing pipelines, transformations, and improving existing solutions
 * Reviews and refines AI-generated outputs to ensure accuracy, efficiency, and alignment with data models and standards
 * Identifies where AI can help reduce manual effort (testing, documentation, repetitive tasks)
 * Uses AI to learn new Snowflake features and improve development practices
 * Uses AI to improve personal development productivity and solution quality

Technical Environment

 * Core Stack: Snowflake (SQL, Stored Procedures, Tasks), Python, Azure Data Factory (ADF), APIs, Blob Storage
 * Operating Environment: Cloud-based (Azure + Snowflake ecosystem)
 * Workflow: Agile delivery, CI/CD (GitLab, Jenkins, Liquibase), ticketing via standard tools (e.g., Jira)
 * Quality & Reliability: Data quality, monitoring, observability, alerting, incident handling