Remote Data Engineer — Azure & Snowflake Pipelines

Mindex

Pennsylvania

On-site

USD 90,000 - 140,000

Full time

14 days+

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Benefits offered by this job

Fully Remote Work Environment

Flexible Time Off

Medical, Dental, and Vision Insurance

401(k) with Company Match

Company-Paid Life and Disability

Professional Development Opportunities

Job summary

Mindex is seeking a Data Engineer to build cloud-based data solutions that power analytics, automation, and AI initiatives. You will design scalable pipelines, integrate data from enterprise systems, and deliver reliable datasets for decision-making.

You will partner with stakeholders to translate business needs into practical data solutions, optimize pipelines, and ensure data quality. The role emphasizes Python, SQL, and modern cloud data platforms in Azure and Snowflake, with fully remote

Qualifications

3 - 5+ years of experience in data engineering, ETL development, or data integration.
Experience with Snowflake or another modern cloud data warehouse, including data modeling, transformations, and performance optimization.
Hands-on experience with Azure Data Factory or a similar cloud-based orchestration tool, including pipeline orchestration and workflow management.
Experience working with Azure Data Lake Storage or similar cloud storage platforms.
Strong SQL skills, including writing complex queries, optimizing performance, and working with large datasets.
Experience developing data solutions using Python.
Understanding of data warehousing concepts, ETL/ELT processes, and dimensional modeling.
Experience integrating data through APIs and working with JSON, XML, or similar data formats.
Experience integrating enterprise SaaS applications such as Salesforce, Workday, GA4, Marketo, or similar business systems

Responsibilities

Design, develop, and maintain scalable data pipelines using Azure Data Factory, Snowflake, SQL, and Python.
Build ETL and ELT processes that integrate data from enterprise applications, APIs, and third-party systems.
Develop reliable datasets and data models that support reporting, business intelligence, automation, and AI initiatives.
Partner with business stakeholders to gather requirements and translate business needs into technical solutions.
Monitor, troubleshoot, and optimize data pipelines to ensure performance, reliability, and data quality.
Perform data validation, testing, and reconciliation to ensure accurate and trusted data.
Document data flows, source-to-target mappings, and technical processes to support ongoing maintenance and knowledge sharing.
Collaborate with cross-functional teams to continuously improve the organization's cloud data platform and engineering practices