Python Data Integration Engineer

Xelerit is hiring a Python Data Integration Engineer to join their Client - actuals Financial infrastructure team focused on high volume digital companies. The role involves direct client system integration to ensure reliable financial data pipelines.

• Minimum 3 years of experience in technical, data-integration, or strong Python data processing.
• Strong proficiency in Python and SQL.
• Hands-on experience or aptitude with orchestration and pipeline tools like Mage.ai, Apache Airflow, dbt, or similar.
• Ability to break down complex, ambiguous integration challenges into pragmatic solutions.
• Strong detail orientation with focus on data accuracy and financial correctness.
• Comfort working directly with technical stakeholders in a fast-paced environment.
• Professional fluency in English (written and spoken).

Nice to have:

• Experience integrating with PSPs (Payment Service Providers), ERPs, or core accounting systems.
• Domain knowledge in accounting, FinOps, or financial data processing.
• Experience with cloud data warehouses such as Snowflake, BigQuery, or Redshift.

Pipeline & Integration Engineering (Primary Focus):

• Design, build, and maintain Python-based data ingestion pipelines using Mage.ai alongside tools like Apache Airflow and dbt.
• Map fields from client systems (orders, payments, payouts, ERPs) into a unified data model using SQL, collaborating with Business Consultants.
• Validate large, high-volume datasets for correctness, completeness, and consistency before and after pipeline runs.
• Troubleshoot pipeline failures and edge cases across varied technical landscapes.
• Document pipeline logic and mapping decisions for handover and reuse.

Client-Facing Technical Delivery:

• Translate ambiguous or incomplete client requirements into robust pipeline designs.
• Guide clients through technical onboarding, working closely with Business Consultants from system discovery to final data delivery.

Product Feedback Loop:

• Identify recurring integration patterns and gaps across client implementations.
• Collaborate with the Product team to develop scalable, reusable productized connectors over time.