About the Role

We are seeking a Customer Facing Data Engineer to join our Data Engineering team and support Wirestock's data infrastructure while acting as a critical technical liaison for our external partners.

This role involves primary Data Engineering duties, including building, optimizing, and maintaining scalable data pipelines and a structured data ecosystem. You will act as a bridge between our technical systems and external partners, helping them effectively use our products, troubleshoot issues, and ensure smooth operations.

You will be responsible for transforming raw data into actionable intelligence, analyzing large-scale datasets, and generating insights to support internal teams, including product, content operations, partnerships, and executive leadership.

This is a night shift position requiring strong data engineering expertise, deep technical troubleshooting skills, and excellent communication. During the night shift, you will directly support clients with complex data-related issues, ensuring smooth data processing, integrations, and product usage.

Working Hours

This is a night shift role with split hours:

4:00 PM - 7:00 PM and 10:00 PM - 3:00 AM

Key Responsibilities

• Serve as the primary technical point of contact for external partners during night shifts, resolving data-related issues via communication channels (email, chat, etc.).
• Troubleshoot and resolve production data pipeline issues, data quality concerns, and complex partner integration problems.
• Analyze and troubleshoot data ingestion and processing failures using technical logs, system outputs, and internal tools.
• Manage and manipulate structured and unstructured data formats, including reviewing and authoring complex JSON configurations for data transformations.
• Perform advanced SQL and NoSQL querying (MySQL, MongoDB) for complex data analysis, integrity checks, and system validation.
• Utilize AWS services (S3) for data governance, retrieval, and management within the data infrastructure.
• Conduct deep-dive data analysis to extract actionable insights that inform product and operational decisions.
• Develop, maintain, and optimize robust data visualizations and dashboards for internal stakeholders.
• Collaborate with Data Engineering, Product, and other teams to define data needs and design scalable reporting solutions.
• Actively contribute to the design, optimization, and documentation of ETL/ELT data pipelines and data governance processes.
• Document recurring client issues, resolutions, and best practices for both the internal knowledge base and external partner use.

Requirements

• Expertise in relational and NoSQL databases, specifically MySQL and MongoDB.
• Advanced SQL proficiency for data manipulation and performance tuning.
• Strong working knowledge of AWS services, particularly S3.
• Proven ability to read, author, and troubleshoot complex JSON configurations.
• Proficiency in Python (or similar scripting language) for data processing and analysis tasks.
• Experience with ETL/ELT concepts and contributing to a modern data platform environment.
• Solid understanding of data modeling, data structures, and fundamental analytical/statistical methods.
• Exceptional written and verbal communication skills, including the ability to translate complex data issues for non-technical clients and internal teams.
• High attention to detail, commitment to data accuracy, and proven analytical/problem-solving skills.
• Fluent in written and spoken English.

Nice to have:

• Basic understanding of Kubernetes and containerized workloads.
• Familiarity with workflow orchestration tools such as Argo Workflows, Airflow, or similar.
• Experience supporting or troubleshooting production data pipelines.
• Experience in BI and data visualization tools (Superset, Metabase, Tableau, Looker, etc.).

Working Conditions

• Night shift position
• Hybrid work environment, combining in-office presence with remote flexibility
• Fast-paced, technically challenging work with real-world data systems