L2/L3 Application Support Engineer - Data Warehouse & ETL (SQL Server, SSIS, SSRS, SSAS)

At Fujitsu, we've been driven to create a sustainable world through innovation since 1935. Today, we lead in digital transformation globally with our 130,000 employees across 50+ countries. We empower our diverse community to achieve greatness through career development and opportunities. Explore our internal positions and join us in shaping a brighter future. Thank you for being a part of Fujitsu. We look forward to growing together toward a brighter future.

1. Position purpose

Provide second- and third-level support for Cleanaway's on-premises Microsoft data warehouse estate: SQL Server data warehouses and operational data stores, SSIS packages scheduled through JAMS and SQL Agent, SSRS reporting, SSAS cubes and Master Data Services. The engineer makes sure daily loads, month-end finance and payroll reporting, and operational and compliance reports are complete and accurate. They resolve incidents and data defects and deliver minor enhancements through change control.

2. Key accountabilities

Incident management and service restoration

• Own assigned ServiceNow incidents through resolution: failed loads, stale or missing data, SSRS availability and report errors, and cube processing failures.
• Diagnose stored procedure, SSIS package and job-chain failures using error logs, monitoring databases and job history. Rerun or recover loads under documented procedures.
• Coordinate with Infrastructure and DBA teams on server restarts, capacity and SQL Server platform issues.
• Resolve emergency finance-integration defects (e.g. accounts-payable voucher and invoice feeds) under emergency change governance.

Monitoring and operational health

• Perform daily checks of overnight ETL, data warehouse load alerts and JAMS/SQL Agent schedules before the business day. Action failures before reporting deadlines.
• Monitor SSAS cube processing and route-planning and operational reporting job chains. Detect stale data before users do.
• Validate data warehouse processing after source-system outages, patching and releases.

Reporting and data operations

• Run scheduled month-end activities: payroll close and labour reporting refreshes for finance, trial balance and BAS extracts, work-hours imports and other recurring finance and HR data runs.
• Investigate report data defects in operational, procurement spend, fleet, driver-compliance and service-performance reporting. Trace lineage from source systems (e.g. JDE) through staging to report.
• Fulfil authorised report access and security requests for SSRS, SSAS roles and Master Data Services.

Change, release and enhancement

• Design, build, test and deploy minor enhancements to SQL objects, SSIS packages, SSRS reports and SSAS cubes, including organisational restructures and new codes or dimensions.
• Prepare change records, test evidence and rollback plans for CAB, and support release validation and smoke testing.
• Support migration of remaining SSIS workloads to Azure Data Factory with the ADF/Databricks engineer: legacy logic analysis, parallel-run reconciliation and decommissioning of retired packages.
• Take delivered project components (e.g. route-planning platform migration) into BAU through hypercare and documented handover.

Problem management and knowledge

• Raise and progress problem records for recurring load failures, report outages and data-quality defects. Document hard-coded business rules in stored procedures and packages.
• Maintain runbooks, job-chain and lineage documentation, report and metric definitions, and knowledge articles. Make sure source code (SSIS, SSRS, SSAS) is held in version control.
• Participate in knowledge transfer from the incumbent provider, demonstrate independent handling of common support scenarios, and transfer knowledge to new team members.

Security and compliance

• Use privileged and production access only through the approved access process (e.g. CyberArk), on a least-privilege basis and for authorised tasks.
• Never record passwords, tokens or other secrets in tickets, email or documents. Store and hand over credentials only through the approved vault.
• Handle customer, employee and payroll personal information in line with the Privacy Act and Cleanaway data classification, including compliant incident records and escalation of potential data breaches.
• Maintain segregation of duties: perform data corrections, approvals and production changes only with documented business or change approval, and keep an audit trail.

3. Role boundaries

L2/L3 accountability

• Own monitoring, incident resolution, data-defect investigation, scheduled reporting runs and minor change delivery across the data warehouse estate.
• Retain ServiceNow ownership when Infrastructure, DBA or vendors remediate underlying platform issues.

Outside delegated authority

• Server, SQL Server instance, backup, patching and SSRS encryption key management owned by DBA and Infrastructure teams.
• Business definition of financial, payroll, procurement or compliance metrics, and approval of data corrections.
• Data platform architecture and modernisation programmes, where the engineer contributes analysis but does not lead.

4. Skills and experience

Essential

• Demonstrated enterprise production support and development experience with SQL Server, T-SQL, SSIS, SSRS and SSAS (multidimensional and/or tabular).
• Experience with enterprise job schedulers (e.g. JAMS) and SQL Agent: dependencies, reruns and recovery.
• Data warehouse concepts: staging, ODS, dimensional modelling, incremental loads and data lineage.
• ITIL-aligned incident, problem, change and request practice using ServiceNow or equivalent.
• Clear communication with finance, operations and reporting stakeholders, including during month-end deadlines.

Desirable

• Master Data Services, JD Edwards data structures and procurement or finance system feeds (e.g. Coupa, Fieldglass).
• Azure Data Factory exposure for SSIS migration and parallel running.
• Power BI datasets sourced from the data warehouse.
• Microsoft data certifications (e.g. DP-300, DP-900), ITIL 4 Foundation.

5. Key relationships

Finance, payroll and procurement teams: Month-end runs, finance extracts and data-defect validation

Operations, fleet and compliance reporting users: Report issues, access and expected outcomes

ADF/Databricks and Power BI engineers: Shared pipelines, migration and downstream reporting dependencies

Service Desk / L1: Intake, initial triage, shift-left knowledge articles and incident escalation

Technical Platform Owner / Principal Analyst: Platform priorities, risks, technical governance and acceptance

Change and release management: CAB approval, controlled releases and production validation

Infrastructure, DBA and Cyber Security teams: Servers, databases, privileged access, certificates and security approvals

6. Performance measures

• Incident response and resolution against agreed service levels and business-critical processing windows.
• Completion and effectiveness of proactive monitoring, exception detection and recovery.
• Service request timeliness and first-time-right fulfilment.
• Changes delivered successfully through change control, without unplanned outage or rollback.
• Reduction in repeat incidents through problem records, root cause analysis and permanent fixes.
• Accuracy and currency of runbooks, knowledge articles, ServiceNow resolution notes and operational documentation.
• Stakeholder satisfaction and timely communication during incidents and changes.
• Daily loads complete before business reporting deadlines. Month-end runs delivered on schedule.

7. Transition readiness (by 21 December 2026)

Independence criteria Fujitsu must demonstrate before taking over from the incumbent provider. Remove this section after transition acceptance.

• Daily ETL and data warehouse monitoring checklist run without incumbent assistance. Alert recipients repointed to Fujitsu groups.
• Resolve at least two load or report failures unaided, including a rerun and recovery.
• Deliver at least one data warehouse, report or cube change end to end through change control before the Christmas change freeze.
• Two month-end cycles (labour reporting and finance extracts) delivered on time.
• A knowledge article for each recurring request type. Problem records with RCA drafted for recurring failures.
• Incident SLAs met for four consecutive weeks.

8. Working arrangements and cross-cover

Provide coverage of Australian business hours (AEST/AEDT) through agreed shift patterns, including the Australian morning window. Participate in a rostered on-call arrangement outside business hours, including weekends, for incidents and business-critical processing windows (e.g. overnight loads, month-end and payroll close), and in planned after-hours changes and maintenance. All operational activities must follow approved procedures, access controls, Cleanaway change governance and delegated authority.

Cross-cover: Secondary cover for the Power BI engineer (datasets and gateways) and for the ADF/Databricks engineer (pipelines feeding the data warehouse). Receives cover from both for data warehouse objects.

At Fujitsu, we are committed to an inclusive recruitment process that values the diverse backgrounds and experiences of all applicants. We believe that hiring people from a wide variety of backgrounds makes us stronger, not because it's the right thing to do, but because it allows us to draw on a wider range of perspectives and life experiences.