Senior Data Engineer

Valsoft Corporation

$100K — $130K *
Enterprise Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • 5-8 years of experience in Data Engineering or related fields.
  • Strong academic background in Computer Science or related quantitative discipline.
  • Experience designing and maintaining large-scale data pipelines.
  • Advanced SQL skills; proficiency in Python or equivalent programming language.
  • Experience with cloud data platforms and modern data stack technologies.
  • Strong understanding of data modeling and distributed data systems.
  • Proven cross-functional collaboration and communication skills.

Responsibilities

  • Design, develop, and maintain scalable data pipelines and ETL/ELT processes.
  • Build and optimize data models, warehouses, and data lakes for analytics.
  • Partner with stakeholders to identify data needs and deliver solutions.
  • Ensure data quality, integrity, governance, and observability.
  • Improve the performance and scalability of the data platform.
  • Support real-time and batch data processing requirements.
  • Collaborate with software engineers to integrate data solutions.

Benefits

  • Opportunity to make a direct impact on a rapidly scaling fintech company.
  • Work in a fast-paced environment with complex data challenges.
  • Collaboration with cross-functional teams.
  • Access to modern data stack technologies and cloud platforms.
  • Potential for professional development in a growing sector.
Full Job Description
The Role

As a Senior Data Engineer, you will be responsible for designing, building, and maintaining scalable data infrastructure that supports analytics, reporting, product development, and operational decision-making. You will work closely with Engineering, Product, Finance, Operations, and Growth teams to ensure reliable, accessible, and high-quality data across the organization.

This role is ideal for someone who thrives in a fast-paced environment, enjoys solving complex data challenges, and wants to have a direct impact on the growth of a rapidly scaling fintech company.

What You'll Do
  • Design, develop, and maintain scalable data pipelines and ETL/ELT processes.
  • Build and optimize data models, warehouses, and data lakes to support business and product analytics.
  • Partner with cross-functional stakeholders to understand data needs and deliver actionable solutions.
  • Ensure data quality, integrity, governance, and observability across all systems.
  • Improve the performance, reliability, and scalability of our data platform.
  • Support real-time and batch data processing requirements.
  • Develop and maintain documentation, standards, and best practices for data engineering.
  • Collaborate with software engineers to integrate data solutions into customer-facing products and internal systems.


What We're Looking For
  • 5-8 years of experience in Data Engineering, Analytics Engineering, or a related field.
  • Strong academic background in Computer Science, Engineering, Mathematics, Statistics, or a related quantitative discipline.
  • Experience designing and maintaining modern data architectures and large-scale data pipelines.
  • Advanced SQL skills and strong proficiency in Python or another relevant programming language.
  • Experience with cloud data platforms and modern data stack technologies.
  • Strong understanding of data modeling, warehousing concepts, and distributed data systems.
  • Proven ability to work cross-functionally and communicate technical concepts to non-technical stakeholders.
  • Experience in payments, fintech, financial services, or transaction-heavy environments is strongly preferred.
  • High attention to detail and a commitment to data quality and reliability.
  • Strong knowledge of Snowflake, DBT, Airflow
  • Strong knowledge and kills in AI development with top LLMs


Nice to Have
  • Experience working within embedded payments, PayFac, merchant acquiring, or payment processing environments.
  • Experience with real-time streaming technologies and event-driven architectures.
  • Familiarity with data governance, compliance, and security requirements in regulated industries.
  • Experience building customer-facing data products or analytics platforms.


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