Remote) Senior Data & Business Intelligence Engineer

Systems & Software, a division of Harris; is seeking a Senior Data and Business Intelligence Engineer who will join the S&S Data Platform & Analytics team.

We are a data-driven organization committed to leveraging information as a strategic asset to improve decision-making, operational efficiency, and business performance of our utility customers. Our Analytics team partners with stakeholders across the organization to develop scalable data solutions that deliver trusted insights and drive measurable customer value.

As we continue to invest in our modern data platform, we are seeking a Senior Data and Business Intelligence Engineer to play a key role in building and evolving our enterprise analytics ecosystem.

The successful candidate will report to the DBA and Analytics Manager.

This remote role welcomes candidates anywhere in Canada and the US. Travel is required as needed, approximately 1-2 times per year to Vermont/Customer Site/Some other destinations. Candidates must hold a current, valid passport and be legally eligible to travel internationally. This includes either passport based visa exemption or possession of any required travel visas for entry into Canada, the United States, and the Caribbean.

Preference will be given to candidates who can work in EST timezone.

80K - 110K

We are committed to leveraging emerging technologies to improve how we work, serve our customers, and drive business outcomes. The successful candidate will demonstrate curiosity and a willingness to actively adopt and leverage AI tools to improve workflows, solve problems, and increase efficiency. Candidates should be comfortable using AI enabled technologies, including copilots, chat based AI assistants, and automation tools, as part of their everyday work while maintaining appropriate judgment, security, and compliance standards.

• Design, build, and support enterprise Data Lake solutions from the ground up.
• Develop scalable ETL solutions and data pipelines.
• Move and process terabytes of data efficiently.
• Implement data quality, data governance, metadata, and lineage processes.
• Develop anomaly detection, monitoring, and alerting capabilities.

• Bachelor's degree in Computer Science, Information Systems, Data Analytics, Engineering, Mathematics, or a related field, or an equivalent combination of education, professional experience, and demonstrated technical expertise.
• 5+ years of experience in Data Engineering, Analytics Engineering, Business Intelligence Engineering, or related discipline.
• Proven experience designing and building enterprise Data Lakes, Lake houses, or analytical data platforms.
• Strong experience developing enterprise ETL/ELT solutions and scalable data pipelines.
• Experience designing, implementing, and supporting modern Data Lakehouse and real-time analytics platforms using technologies such as Oracle Database, Debezium, Kafka, Kafka Connect, Apache Flink, Apache Iceberg, Nessie, Star Rocks, Spark, Kyuubi, and Power BI. Experience building scalable, reliable data pipelines that support both batch and streaming data workloads is highly desirable.
• Experience with Change Data Capture (CDC), event-driven architectures, real-time data processing, data cataloging, schema evolution, data versioning, and enterprise-scale ETL/ELT solutions.
• Experience supporting large-scale data ingestion, transformation, reconciliation, backfill processes, and distributed analytics platforms, including troubleshooting and performance optimization of production data services.
• Familiarity with observability, monitoring, and Site Reliability Engineering (SRE) practices using technologies such as Prometheus, Victoria Metrics, and Grafana.
• Strong experience supporting production Linux environments (Oracle Linux, RHEL, CentOS, or equivalent), including system administration, systemd-managed services, performance tuning, automation, monitoring, troubleshooting, and operational support of distributed data and analytics platforms.
• Advanced SQL development, data modeling, and performance tuning skills.
• Experience with dimensional modeling, semantic-layer design, and analytical data structures.
• Experience processing and managing large-scale data environments.
• Hands-on experience implementing data quality, reconciliation, monitoring, and observability frameworks.
• Experience with metadata management, data lineage, governance, and data cataloging practices.
• Experience implementing monitoring, alerting,…