QA Automation (DataOps) Team Lead to $6800

Who We Are: Adaptiq is a technology hub specializing in building, scaling, and supporting R&D teams for high-end, fast-growing product companies in a wide range of industries.  

About the Product: Our client is a fast-growing data-driven company operating in the mobile ecosystem. Their platform leverages advanced machine learning and big data technologies to help apps reach and engage the right audiences at scale. Handling tens of terabytes of data daily and serving millions of requests per second, their infrastructure powers intelligent, real-time decision-making across billions of user interactions. With near-zero churn and strong growth momentum, this company offers a unique opportunity to work on complex, large-scale data systems that directly influence global business outcomes.

About the Role:

We are looking for a DataOps Team Lead - a professional with a solid background in QA automation or data engineering, eager to transition into leading automation and observability at the data infrastructure level.

In this role, you will ensure the accuracy, reliability, and performance of large-scale data pipelines. You’ll design frameworks for automated data validation, build observability processes, and enable internal teams to trust and act on data confidently. This position is ideal for QA Automation Leads or Engineers ready to take the next step into DataOps leadership, combining their technical expertise with a passion for process excellence and data quality.

Key Responsibilities: 

• Lead a DataOps team focused on data quality, observability, and reliability.
• Monitor, analyze, and troubleshoot large-scale data processes; identify and resolve data inconsistencies and performance bottlenecks.
• Develop and maintain automation frameworks for data validation and testing using Python, Airflow, and SQL.
• Apply QA automation principles to the data lifecycle, ensuring robust data workflows and continuous quality assurance.
• Create scripts and queries for ad-hoc operational support — data extraction, configuration, and validation.
• Manage metadata systems, defining data lineage, dependencies, and relationships for full transparency.
• Collaborate with Data Engineering, BI, and Operations teams to improve data reliability and service performance.

Required Competence and Skills:

• Bachelor’s degree in Computer Science, Engineering, or a related technical discipline
• 5+ years of engineering experience in production
• 3+ years of hands-on experience with automation frameworks and scripting in Python
• At least 1 year of experience in a team leadership or mentorship role
• Strong understanding of SQL/NoSQL databases and data warehouses (e.g., MySQL, Presto, Athena, MongoDB)
• Familiarity with test automation, CI/CD integration, and production monitoring
• Proven ability to manage and improve complex technical processes with a high degree of ownership and accountability
• Strong organizational and communication skills with a proactive, product-oriented mindset

Nice to have:

• Experience with PySpark or other distributed data processing systems.
• Background in data observability, pipeline monitoring, or data lifecycle automation.
• Experience in a fast-paced, data-intensive environment.