Systems Engineer

· Design, develop, and maintain large-scale data processing pipelines using Apache Spark.

· Monitor and troubleshoot Spark job failures, including driver/executor crashes and performance bottlenecks.

· Manage and optimize workloads running on Hadoop (HDFS, YARN) clusters.

· Provide support for onboarding new data pipelines and services into the platform.

· Analyze and resolve resource allocation issues such as CPU/memory quota exceedance in Kubernetes environments.

· Build and maintain ETL pipelines for ingesting, transforming, and loading large datasets.

· Ensure data quality, consistency, and integrity across distributed data systems.

· Implement alerting rules and thresholds using monitoring platforms (e.g., Prometheus-based systems).

· Work with Kafka to manage data streaming pipelines, including topic configuration and access control.

· Troubleshoot Kafka consumer/producer issues, including lag, permissions, and connectivity errors.

· Implement and maintain data retention policies in Lakehouse architectures.

· Perform log analysis and debugging using distributed logging tools.

· Coordinate with infrastructure teams to resolve cluster-level or networking issues.

· Configure and manage storage paths, table-level retention, and lifecycle policies for datasets.

· Develop and execute SQL queries for data analysis, validation, and reporting.

· Automate workflows and monitoring using Python scripts and APIs (e.g., GitHub API, SQLAlchemy).

· Continuously improve system efficiency, scalability, and cost optimization.

· Analyze alerts from monitoring systems and take proactive action to prevent outages.

· Investigate production incidents (P1/P2) and perform root cause analysis (RCA).

· Collaborate with cross-functional teams (developers, SREs, data engineers) to resolve system issues.

· Conduct data validation and reconciliation between upstream and downstream systems.

· Maintain dashboards and observability tools (e.g., Hubble, internal monitoring systems).

· Optimize performance of distributed jobs by tuning configurations and execution plans.

· Handle identity and access management issues across systems (Kerberos, service accounts, ACLs).

· Support migration and integration of new data technologies into existing ecosystems.

· Work on Kubernetes-based Spark deployments and troubleshoot pod scheduling and quota issues.

· Ensure high availability and reliability of data pipelines and streaming jobs.

· Participate in on-call rotations and respond to critical production alerts.

· Document system architecture, troubleshooting steps, and operational procedures.

· Ensure compliance with organizational data governance and security standards.

Do you have experience in Python?, Do you have a Bachelor’s degree?, Bachelor’s Degree is required in Computer Science or Computer Engineering or Computer Information Systems or Information Technology or Data Science.