DataOps Engineer
This role is dedicated to the operational maintenance, governance, and delivery lifecycle of data assets. Unlike infrastructure-focused roles, the DataOps Engineer is responsible for user access provisioning, data platform permission management, deployment releases of analytical pipelines, and service observability (monitoring/alerting) to guarantee data availability and compliance without managing foundational cloud or network architecture.
This role involves active monitoring, incident resolution, design incident prevention plan and engagement with data requests while continuously improving data processes and systems, providing essential support that ensures optimal data quality.
- Manage and audit Service Accounts, AWS IAM policies for data pipelines, and Role-Based Access Control (RBAC) schemas and Role & Permission management on Snowflake.
- Execute and automate deployment releases for data pipeline codebases, orchestration logic and database schemas.
- Configure, monitor, and optimize pipeline alerting thresholds in Amazon CloudWatch and Coralogix to log task execution failures and data delivery delays.
- Manage workflows, task schedules, and environment permissions using Apache Airflow.
- Actively design and implement preventive operations strategies, analysing system trends, pipeline bottlenecks, and job failures to eliminate systemic risks before SLA breaches occur.
- Formulate and maintain sophisticated observability patterns, building real-time dashboards and threshold-based notification alerts inside Coralogix to flag delays, capacity exhaustion, or anomalies.
- Perform root-cause analysis on runtime pipeline anomalies and complex data flow errors, collaborating across analytics engineering teams to design permanent, scalable operational remediation.
- Perform rigorous investigations into data quality, identifying discrepancies and implementing corrective actions in collaboration with other teams.
- Collaborate on data engineering initiatives, focusing on the enhancement of existing processes and the development of new solutions to improve overall data workflow.
- Engage in incident monitoring and resolution, escalating more complex issues to second line support as necessary.
- Monitor query runtimes and compute consumption metrics to identify underutilized resources, proposing optimizations to maintain technical excellence and cost-efficiency.
- Manage incidents and perform on-call duties on related Technologies required at the Role.
- Proactively identify opportunities to leverage AI, intelligent agents and automation to streamline operations, accelerate incident investigations and improve platform reliability.
- Good experience configuring security structures, service integration privileges, and RBAC governance within enterprise data layers (e.g., Snowflake and AWS IAM). (required)
- Proven expertise managing, deploying workflows, and debugging task execution failures in Apache Airflow. (required)
- Advanced skill in engineering cloud monitoring rules, logging metrics, and automated alert structures using Amazon CloudWatch and Coralogix. (required)
- High proficiency in writing complex SQL queries for dataset analysis and metadata tracking. (required)
- Strong scripting capability in Python or shell environments to automate operational checklists, administrative tasks, and compliance auditing. (required)
- Must hold a valid AWS Certification focused on Operations/Support (such as AWS Certified SysOps Administrator). (required)
- A valid Snowflake Certification (such as Snowflake SnowPro Core Certification) to demonstrate deeply validated platform and data governance knowledge. (required)
- Familiarity with broader modern data environments (AWS Glue, S3, Athena and Redshift). (nice-to-have)
- Exposure to data observability concepts and data contract mechanisms. (nice-to-have)
- Good Data Analysis experience to identify trends and track Data Quality issues. (nice-to-have)
- Excellent verbal and written English Communication skills. (nice-to-have)
- Strong interpersonal and communication skills with a customer-centric mindset; ability to build positive relationships with internal clients (data engineers, analysts, and business stakeholders) and translate complex technical operation issues into clear, actionable updates. (nice-to-have)
- Strong self-learning mindset: a highly proactive professional driven to explore new technologies, documentation, and industry best practices independently to continuously evolve their skill set. (nice-to-have)
- Able to set priorities, anticipate risks, and multitask under pressure. (nice-to-have)
- Proficiency in SQL for querying and managing datasets. (nice-to-have)
- Experience with scripting languages to automate and streamline tasks. (nice-to-have)
- Familiarity with monitoring tools and incident resolution methodologies. (nice-to-have)
- Reliability and strong accountability in managing data operations and supporting team objectives. (nice-to-have)
- A proactive approach to problem-solving with a strong attention to detail. (nice-to-have)
- Committed to maintaining high standards of data quality and integrity. (nice-to-have)
Betsson is a diversified, multinational gaming group whose history dates back to 1963 and which is now listed on Nasdaq Stockholm. The group employs around 3,000 people of more than 75 nationalities across over 20 locations; Betsson AB is registered in Stockholm, while its operational headquarters in Ta' Xbiex, Malta, run the day-to-day business. Through brands such as Betsson, Betsafe and NordicBet, it offers casino, sportsbook and other gaming products in regulated markets across Europe, the Americas and Central Asia. Its proprietary technology supports a scalable model serving both B2C customers and B2B partners, with responsible growth and customer protection central to its strategy.