Senior Software Engineer I
Do you enjoy building modern software solutions using a diverse technology stack?
Do you enjoy mentoring colleagues, sharing knowledge, and contributing to a collaborative, high-performing engineering team?
About the Business
LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within our Business Services vertical, we offer a multitude of solutions focused on helping businesses of all sizes drive higher revenue growth, maximize operational efficiencies, and improve customer experience. Our solutions help our customers solve difficult problems in the areas of Anti-Money Laundering/Counter Terrorist Financing, Identity Authentication & Verification, Fraud and Credit Risk mitigation and Customer Data Management. You can learn more about LexisNexis Risk at https://risk.lexisnexis.com/
About the Team:
Team is tech hub for rest of the organization and hence working across multiple domains – US Healthcare, Insurance, Government etc. Team primarily works on data extraction, manipulation, loading, and analytics using technologies like Python, PySpark, Bigdata – HPCC, language ECL, Talend, Azure tools (Data Synapse, ADF etc), Power BI, Essbase, OCI, etc.
About the Role
We are seeking a Data Platform Operations & Observability Engineer to enhance and support our analytics platform across on-premises and Azure environments. This role requires reliable support for on-premises and Azure analytics platforms through pipeline monitoring, incident resolution, automation, and observability improvements. The role requires strong Python, SQL, cloud, data engineering, monitoring, troubleshooting, and maintaining data pipelines and platform services. The engineer will investigate production issues, restore failed processes, communicate with stakeholders, and help improve platform reliability. Over time, the engineer will also build and enhance monitoring, alerting, logging, dashboards, and operational processes to improve observability and reduce incidents.
Responsibilities
Operations & Support
- Monitor and support production data pipelines and analytics platforms and Identify opportunities to improve reliability, performance, and operational efficiency.
- Troubleshoot pipeline failures, data issues, application errors, and performance problems, data movement across on-premises and cloud environments.
- Perform root cause analysis and implement corrective actions and Communicate incidents, remediation plans, and service impacts to stakeholders.
- Support Python automation processes, scheduled jobs, and file transfer workflows. Administer Windows servers, including patching, software installation, vulnerability remediation, and system maintenance.
- Support Azure services including Databricks, Data Factory, Synapse, and Data Lake Storage. Create and maintain operational documentation, diagrams, and support procedures.
Observability & Reliability
- Design and maintain monitoring, alerting, and dashboard solutions. Build and maintain centralized logging and operational reporting.
- Define and track operational metrics, KPIs, SLAs, and SLOs and develop proactive monitoring to detect issues before they affect users.
- Automate operational tasks and repetitive support activities drive continuous improvements in platform reliability and observability.
Required Skills & Qualifications
- Strong Python development and troubleshooting skills with Pyspark also expected with Observability is considered the key requirement, including logging, monitoring, dashboard creation from logs, alerting, debugging, and performance analysis.
- Have relevant experience in cloud logging tools such as Grafana and Elasticsearch/Kibana, Azure Log Analytics Workspace and Amazon CloudWatch Logs.
- Experience working with Azure or other cloud platforms also and using common data formats such as CSV, JSON, XML, and Parquet.
- Experience administering Windows Server environments, Knowledge of data integration concepts, file transfers, APIs, and data validation. Understanding of networking fundamentals including DNS, proxies, firewalls, VPNs, and SSL/TLS.
- Strong troubleshooting and analytical skills, verbal, and written communication skills with ability to manage multiple priorities in a fast-paced environment, Self-motivated, detail-oriented, and eager to learn.
Preferred Qualifications
- Scheduling tools: Control-M, AutoSys, Airflow, Windows Task Scheduler, and Job Scheduler were discussed. Experience with these would be beneficial.
- Nice-to-have experience: Databricks and Azure Data Factory experience are not mandatory but would be beneficial.
- Preferrable from NOC/networking, administration, development, or data-engineering backgrounds who have moved toward Python and observability-oriented work.
Our Commitment to Inclusion
We welcome applications from individuals of all backgrounds, including underrepresented groups, people with disabilities, career changers, and candidates with non-traditional education or experience. If you require accommodations at any stage of the recruitment process, please let us know. We are committed to supporting your success.
Learn more about the LexisNexis Risk Solutions team and how we work
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