What You'll Do

Use our Data Studio and Data Platform to build and manage end-to-end data flows across connection, extraction, mapping, transformation, validation, and publishing.
Analyze source system data to understand file structures, APIs, data relationships, and model requirements for new integrations and use cases.
Connect FloQast to customer source systems including ERPs, file-based sources, APIs, and other upstream systems using supported connectivity patterns such as sFTP, APIs, and data sharing.
Create mappings for new source systems and new FloQast data models, ensuring source data is accurately normalized for downstream application use.
Map and transform source data into FloQast data models so it can be consumed consistently across FloQast applications.
Build custom data models and transformation logic that extend FloQast's surface area to new systems and use cases, including non-ERP systems such as subledgers, banks, billing, payments, and other external platforms.
Partner cross-functionally with customers, partners, Implementation, Customer Success, Product, and Engineering to turn business needs into working integrations and durable data solutions.
Troubleshoot mapping, transformation, and publishing issues across customer implementations and internal platform workflows.
Resolve data variances between source systems and published outputs to ensure accuracy, consistency, and trust in the data.
Support successful customer launches by ensuring source data is connected, validated, and published accurately into FloQast applications.
Scale integration patterns, mappings, and recipes so new sources can be onboarded more efficiently and consistently over time.
Identify opportunities to improve Data Studio recipes, platform capabilities, and operating processes so integrations become faster, more scalable, and easier to support.

What You'll Bring

3+ years of experience in data integration, technical implementation, solutions consulting, analytics engineering, data migration, or a similar hands-on role.
SQL, Python, and scripting skills, with hands-on experience working with data pipelines, APIs, file analysis, and file manipulation.
Advanced proficiency in Microsoft Excel.
Experience working with large CSV, TSV, and Excel-based source files.
Experience working with structured and semi-structured data, transformation logic, schema mapping, and data validation.
Experience cleansing, reshaping, and validating data for downstream consumption.
Comfort working with incomplete, inconsistent, or messy source data.
Strong communication skills and the ability to work effectively across customers, partners, Implementation, Customer Success, Product, and Engineering.
Comfort operating in ambiguity and moving quickly without heavy process or close oversight.
Strong debugging and troubleshooting ability across end-to-end data flows, including identifying root causes and driving issues to resolution.
A practical ownership mindset with a bias toward getting working data into production safely and reliably.

Nice to have

Experience working with financial data models and systems, such as hands-on experience with one or more major ERPs or accounting systems such as Oracle, SAP, Workday, or OneStream.
Working knowledge of accounting and financial data, with the judgment to recognize when balances, relationships, or hierarchies do not make business sense.
Experience working in a model-driven or metadata-driven data platform where source data is mapped, normalized, and published into reusable application-ready models.
Experience creating custom transformation logic or custom data models to support downstream reporting, operational workflows, analytics, or application use cases.
Experience troubleshooting customer-specific data issues in production environments, including source data defects, mapping gaps, schema mismatches, and transformation failures.