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Valsoft is looking for a Data Engineer with approximately 2 years of hands-on experience to join our Finance & Acquisition Data and Reporting team under the Finance Department at Valsoft. In this role, the candidate is responsible for designing, building, and maintaining scalable data pipelines and analytics infrastructure that support financial, acquisition, and deal flow reporting, forecasting, and decision-making across our portfolio of companies. You will work closely with finance, M&A, reporting, and engineering stakeholders to ensure reliable, high-quality data flows from source systems to our analytics platforms.
Job Responsibility:
Design, build, and maintain robust ETL/ELT data pipelines
Develop and optimize data models in Snowflake using dbt
Ingest data from multiple sources using Stitch or Fivetran
Orchestrate and monitor workflows using Apache Airflow
Write efficient and well-documented SQL and Python code
Ensure data quality, reliability, and performance across pipelines
Work with AWS tools (Lambda, S3, IAM, API Gateway, etc.)
Build API integrations between systems and the data warehouse
Collaborate with finance stakeholders to support reporting, analytics, and forecasting
Troubleshoot data issues and improve pipeline reliability
Follow best practices for version control, testing, and deployment
Perform other relevant tasks/projects assigned by the manager
Requirements:
~2 years of professional experience as a Data Engineer or similar role
Strong hands-on experience with: Snowflake
dbt
AWS (S3, IAM, Lambda, etc.)
Stitch and/or Fivetran
Apache Airflow
Cloud technologies
Power BI (preferred) or Tableau
Strong proficiency in SQL
Working experience with Python
Solid understanding of: Relational databases and data warehousing concepts
Data pipelining and ETL/ELT frameworks
Experience with production data systems
Comfortable handling structured and semi-structured data files like JSON, Parquet, XML
Nice to have:
Experience or domain knowledge in the finance industry and/or M&A
Exposure to application development
Familiarity with AI/machine learning concepts or data preparation for AI
Experience supporting financial reporting, forecasting, or accounting data
Knowledge of data governance, security, or compliance practices