Data Engineer

Expert Technical Solutions; EE. UU.; A distancia; USD 110.000 - 150.000; Jornada completa

Descripción de la vacante

Expert Technical Solutions seeks a Data Engineer for an immediate 6-month contract with a strong possibility of extension to permanent. The role is fully remote from home in the United States.

You will design, build, and operate intelligent data pipelines and infrastructure to power ML and AI-driven product capabilities. This position spans data engineering, applied AI, and production-grade ML engineering, collaborating with Product, BI, and Data teams to establish standards and deliver

Formación

• 3–5 years of data engineering experience, preferably on AWS cloud data platforms.
• Hands-on experience building, maintaining, and supporting cloud-based data pipelines.
• Strong knowledge of PySpark, Databricks, and SQL.
• Proficient in Python and data modeling, data warehousing, and analytics concepts.
• Experience with CI/CD practices, automated deployment, and unit testing.

Responsabilidades

• Design, build, and maintain scalable cloud-based data pipelines and data infrastructure.
• Deliver high-quality data models and curated datasets for analytics and reporting.
• Optimize Spark, PySpark, and SQL workloads for performance and cost efficiency.
• Support production data pipelines via monitoring and incident resolution.
• Document data flows, pipeline logic, and operational processes.

Conocimientos

PySpark

SQL

Python

Data modeling

Data warehousing

ETL/ELT

CI/CD

AWS cloud

Databricks

Spark

Troubleshooting

Herramientas

Databricks

AWS

Spark

Kafka

Kinesis

Descripción del empleo

Expert Technical Solutions has an immediate opening for a Data Engineer with one of our premier clients in the financial services industry.

This is 6 months + contract with a strong possibility of extension on conversion to permanent.

The position is fully Remote from home.

You will be responsible for designing, building, and operationalizing intelligent systems that extract value from data and power ML and AI-driven product capabilities.

This role spans data science, applied AI (including generative AI and LLM-based systems), production grade machine learning engineering and advanced analytics.

The Data Scientist partners with Product, BI, and Data Engineering teams to establish data science and machine learning standards, architect scalable AI systems, and deliver measurable business impact across internal and customer-facing solutions.

Responsibilities:

• Design, build, and maintain scalable, reliable cloud-based data pipelines and data infrastructure.
• Deliver high-quality data models and curated datasets that support analytics, reporting, and data-driven decision-making.
• Optimize Spark, PySpark, and SQL workloads to improve performance, reliability, cost efficiency, and scalability.
• Support production data pipelines through monitoring, troubleshooting, incident resolution, and continuous improvement.
• Implement data engineering standards, CI/CD practices, automated deployment processes, unit testing, and code quality expectations.
• Partner with BI, Product, Engineering, and client-facing teams to translate business and reporting requirements into scalable data solutions.
• Document technical solutions, data flows, pipeline logic, and operational processes to support knowledge sharing and long-term maintainability.
• Design, build, maintain, and optimize data pipelines using Python, SQL, PySpark, Databricks, and AWS-based data services.
• Develop ETL/ELT processes that support data warehousing, analytics, reporting, and business intelligence use cases.
• Build and optimize Spark jobs, with a focus on performance, scalability, reliability, and efficient resource utilization.
• Design and implement data models for structured, semi-structured, and NoSQL data where applicable.
• Implement CI/CD practices, automated deployments, unit tests, and code quality standards for data engineering workflows.
• Monitor, troubleshoot, and support production data pipelines, resolving issues and recommending improvements.
• Collaborate with BI Analysts, Product, Engineering, Data, and client-facing teams to understand requirements and support reporting needs.
• Document technical solutions, data flows, pipeline logic, and operational processes.

Experience:

• Minimum of 3-5 years of experience in Data Engineering, preferably working with AWS-based cloud data platforms.
• Hands‑on experience building, maintaining, and supporting cloud-based data pipelines.
• Strong knowledge of PySpark, preferably on the Databricks platform.
• Hands‑on experience with Databricks.
• Strong proficiency in SQL, including query optimization.
• Strong proficiency in Python.
• Strong knowledge of data modeling, data warehousing, ETL/ELT, and analytics concepts.
• Experience with CI/CD practices, automated deployment processes, unit testing, and code quality standards.
• Experience troubleshooting, monitoring, and supporting production data pipelines.
• Experience documenting technical solutions, data flows, and pipeline logic.
• Strong analytical and problem‑solving skills, with the ability to translate business requirements into scalable data solutions.

Preferred:

• Apache Kafka experience.
• Experience with streaming technologies such as Kafka or Kinesis.
• Familiarity with an analytics/visualization platform such as Power BI or Tableau.