Data Analytics Engineer

TechDigital Group; Secaucus (NJ); On-site; USD 100,000 - 130,000; Full time

Job summary

A data solutions company is seeking a Data Analytics Engineer with strong experience in Google Cloud Platform (GCP) to design, build, and optimize scalable data pipelines. The role involves ensuring data quality and enabling advanced analytics and reporting. Key responsibilities include maintaining ETL/ELT pipelines and collaborating with data scientists and business teams to deliver insights. Candidates should have expertise in BigQuery, Dataflow, Python, and data governance practices.

Qualifications

• Strong experience in Google Cloud Platform (GCP).
• Expertise across modern data engineering and analytics tools.
• Experience in designing and building scalable data pipelines.

Responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines.
• Implement data modeling and transformation best practices.
• Collaborate to deliver insights through Looker dashboards.

Skills

Google Cloud Platform (GCP)

BigQuery

Dataflow

Dataform

Looker

Python

Job description

Overview

Job Title: Data Analytics Engineer with strong experience in Google Cloud Platform (GCP) and expertise across modern data engineering and analytics tools. The ideal candidate will design, build, and optimize scalable data pipelines, ensure data quality, and enable advanced analytics and reporting through BigQuery, Dataflow, Dataform, Looker, and Python. This role bridges data engineering and analytics, ensuring reliable, accurate, and actionable insights for business stakeholders.

Key Responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines using Dataflow, Dataform, and BigQuery.
• Implement data modeling, transformation, and orchestration best practices for analytics workflows.
• Collaborate with data scientists, analysts, and business teams to deliver insights via Looker dashboards and reports.
• Ensure data reliability, quality, and governance across multiple data sources.
• Optimize BigQuery queries, partitions, and clustering for performance and cost efficiency.
• Write Python scripts for automation, data processing, and integration with APIs.
• Implement CI/CD practices for data pipeline deployments.
• Monitor, troubleshoot, and improve data infrastructure performance and reliability.
• Document data models, pipelines, and dashboards to ensure transparency and maintainability.