Associate Data Engineer - Voice of the Customer AnalyticsPosition Overview The Associate Data Engineer will support data engineering, analytics, and reporting initiatives for Voice of the Customer and related business programs. This role is designed for an early-career data professional with strong hands-on experience building data pipelines, supporting data quality, working with cloud-based data platforms, and partnering with analytics and business teams to deliver reliable data for reporting, dashboards, and business insights.
The ideal candidate will bring internship or early professional experience in data engineering, business analytics, cloud data platforms, and modern ELT/ETL practices. This role will work closely with business stakeholders, analytics teams, and technical teams to help collect, transform, validate, document, and deliver data that supports customer experience, operational reporting, and decision-making.
Key Responsibilities - Support development and maintenance of data pipelines used for customer insights, reporting, analytics, and business intelligence.
- Assist with data ingestion, transformation, validation, and refresh processes across cloud and data warehouse platforms.
- Build and maintain SQL-based data models, data transformations, and reporting-ready datasets.
- Support data quality checks, testing, reconciliation, and issue resolution to improve trust in downstream reporting.
- Partner with analytics, operations, and business stakeholders to understand data needs and translate them into technical requirements.
- Help document data flows, data lineage, data contracts, transformation logic, and reporting dependencies.
- Support dashboards, reporting datasets, and analytics outputs used by business and leadership teams.
- Assist with automation of manual data refresh, validation, and reporting processes.
- Use version control and development best practices to support repeatable and reliable data delivery.
- Collaborate with cross-functional teams to troubleshoot data issues, improve pipeline reliability, and support timely delivery of analytics solutions.
- Continue building technical depth across cloud platforms, data engineering tools, business analytics, and customer data use cases.
Required Qualifications - Bachelor's degree in engineering, analytics, computer science, information systems, data science, business analytics, or a related field.
- Internship, academic project, or professional experience in data engineering, analytics engineering, business analytics, or data pipeline development.
- Strong SQL skills with experience querying, transforming, validating, and analyzing data.
- Experience with Python or PySpark for data processing, automation, testing, or analytics.
- Exposure to cloud data platforms or cloud services such as AWS, GCP, Snowflake, BigQuery, Databricks, or similar.
- Experience or academic exposure to ETL/ELT pipelines, data warehousing, data modeling, or reporting datasets.
- Understanding of data quality, testing, documentation, and data validation practices.
- Ability to work with business and technical teams to gather requirements and support reporting or analytics needs.
- Strong analytical, problem-solving, communication, and documentation skills.
- Ability to learn quickly, take ownership, and work in a collaborative team environment.
Preferred Qualifications - Experience with Snowflake, dbt, AWS Glue, S3, Airflow, Terraform, Git, or similar modern data engineering tools.
- Exposure to customer analytics, Voice of the Customer, survey data, customer feedback, product quality, or business operations data.
- Experience supporting dashboards or reporting solutions using Tableau, Power BI, Superset, or similar BI tools.
- Experience with data contracts, data lineage, data quality checks, or automated validation.
- Exposure to CI/CD or version-controlled data pipeline development.
- Cloud certification such as AWS Solutions Architect Associate, Google Associate Cloud Engineer, or similar.
- Experience with machine learning pipelines, predictive analytics, NLP, or customer sentiment/defect analysis is a plus.
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