Integration & Data Movement Engineer

Openkrill; United States; Remote; USD 130.000 - 180.000; Vollzeit

Zusammenfassung

Openkrill seeks an Integration & Data Movement Engineer to design, implement, and operate reliable data flows across CRM, ERP, finance, HR, analytics, and partner platforms. The role focuses on ELT/ETL pipelines, Snowflake-based ingestion, and Medallion Architecture to ensure secure, scalable data movement.

You will transform data with SQL and Python, build reusable ingestion frameworks, and enforce data quality. Collaboration with analytics teams and documentation of designs are key outcomes.

Qualifikationen

• 5–10+ years designing and implementing data integrations and pipelines.
• Strong SQL skills, including stored procedures.
• Strong Python development for ingestion, transformation, and automation.
• Hands-on Snowflake experience.
• Experience with APIs, relational DBs, flat files, and external systems.
• Experience ELT/ETL solutions and data quality controls.
• Knowledge of REST/SOAP APIs and integration patterns.

Aufgaben

• Design and implement integrations and ELT/ETL pipelines across APIs, databases, and SaaS platforms.
• Build and maintain Snowflake-based ingestion and transformation processes.
• Apply Medallion Architecture to organize data across raw, curated, and business-ready layers.
• Develop data transformation logic using SQL and Python.
• Create scalable ingestion frameworks supporting batch and incremental loads.
• Implement data quality checks, validation, monitoring, and error handling.
• Document designs, lineage, dependencies, and operational procedures.
• Partner with analytics teams to ensure trusted data for reporting.

Kenntnisse

SQL

Python

ETL/ELT

REST/SOAP APIs

Data Modeling

Data Pipelines

Tools

Snowflake

DBT

Kafka

Service Bus

Cloud Platforms (Azure/AWS/GCP)

Jobbeschreibung

Integration & Data Movement Engineer(Enterprise Integrations & Data Flow Enablement)

Role OverviewWe are seeking a highly skilled Integration & Data Movement Engineer to design, implement, and operate reliable data flows between enterprise platforms. This role is responsible for ensuring that data moves accurately, securely, and efficiently across systems such as CRM, ERP, finance, HR, analytics, and external partner platforms.

Key Responsibilities

• Design and implement integrations and ELT/ETL pipelines across APIs, databases, file-based sources, SaaS platforms, and enterprise applications.
• Build and maintain Snowflake-based data ingestion and transformation processes.
• Apply Medallion Architecture principles to organize data across raw, curated, and business-ready layers.
• Develop data transformation logic using both SQL and Python, selecting the best tool based on business and technical requirements.
• Create scalable and reusable ingestion frameworks supporting batch and incremental loads.
• Implement data quality checks, validation rules, monitoring, and error handling.
• Partner with analytics and reporting teams to ensure data is trusted and optimized for downstream consumption.
• Document integration designs, lineage, dependencies, and operational procedures.

Required Qualifications

• 5–10+ years of experience designing and implementing system integrations and data pipelines.
• Strong SQL expertise including Stored Procedures.
• Strong Python development skills for data ingestion, transformation, automation, and operational support.
• Hands‑on experience with Snowflake.
• Experience ingesting and transforming data from APIs, relational databases, flat files, and external systems.
• Experience implementing ELT/ETL solutions and data quality controls.
• Strong understanding of REST/SOAP APIs, authentication mechanisms, and integration patterns.

Preferred Qualifications

• Experience with DBT, including models, testing, lineage, documentation, and data quality frameworks.
• Experience with cloud-native integration patterns and services (Azure, AWS, or GCP).
• Familiarity with Kafka, Service Bus, or other event streaming technologies.

What Success Looks Like

• Reliable, scalable, and well-governed data pipelines supporting enterprise reporting and analytics.
• Consistent application of Medallion Architecture and data engineering best practices.
• Trusted, documented, and testable transformation logic with strong lineage visibility.
• Efficient onboarding of new data sources with minimal manual intervention.