Cloud Data Engineer for Scalable Analytics Pipelines

Metas Solutions

United States

On-site

USD 110,000 - 160,000

Full time

3 days ago

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Benefits offered by this job

Medical, Dental, Vision

Life Insurance

401K with company match

Paid Time Off

Job summary

Metas Solutions seeks a Data Engineer to design, build, and optimize cloud-based data pipelines and data stores enabling analytics across large datasets. You will transform raw data into analysis-ready formats, implement end-to-end ETL/ELT workflows, and ensure data quality throughout the pipeline lifecycle, collaborating with analysts, product teams, and developers.

The role requires 3+ years in data warehouse development and strong Python/SQL skills, cloud experience, and the ability to obtain

Qualifications

3+ years of experience in Data Warehouse development.
3+ years of experience with data analysis of aggregated data from multiple sources in cloud-based environments.
2+ years of experience developing and maintaining scalable data stores that supply big data in forms needed for business analysis.
Experience with SQL and Python for retrieving, parsing, and processing structured and unstructured data.
Experience developing scalable ETL or ELT workflows for reporting and analytics using cloud-based technologies.
Experience with using Cloudera or Hadoop technologies to ingest, transform, and process data.
Experience leveraging generative AI coding tools to assist in development.
Ability to perform backend SQL and NoSQL database testing, including post-deployment and data validation testing.
Ability to obtain and maintain a Public Trust or Suitability/Fitness determination based on client requirements.
Bachelor's degree
Must be a U.S. Citizen or Lawful Permanent Resident (Green Card Holder)

Responsibilities

Design, develop, and maintain scalable ETL/ELT pipelines to ingest, transform, and deliver structured and unstructured data from multiple cloud-based sources.
Build and optimize data storage solutions (data warehouses, data lakes, or lake houses) that support large-scale analytics, reporting, and business intelligence needs.
Perform backend SQL and NoSQL testing, including data validation, post-deployment checks, and troubleshooting of ingestion or schema issues.
Collaborate cross-functionally with analysts, engineers, and stakeholders to translate requirements into technical data models, workflows, and pipeline specifications.
Develop scripts and automation to convert diverse data types into usable formats, while scaling, monitoring, and operating cloud-based data platforms.