30 Siepe Senior Ui Software Engineer Jobs Hiring Near You

• Lead and mentor a team of engineers responsible for building custom, client-specific solutions; recruit and grow the team as Professional Services scales. 
• Own end-to-end technical delivery for Professional Services initiatives—from intake and feasibility review through development, validation, deployment, and post-release supportability. 
• Run technical review and estimation: validate feasibility, surface assumptions, identify dependencies, and produce credible schedules; establish “delivery contract” discipline (scope boundaries, acceptance criteria, change control).
• Drive delivery accountability to commitments, timelines, and quality standards; proactively manage risk and communicate tradeoffs early. 
• Partner tightly with Support and Client Specialization to get work done: establish an engagement model (intake → triage → prioritization → delivery → release windows); define escalation paths, SLAs, and operational handoffs.
• Build and oversee data-driven solutions—custom reports, exports, dashboards, performance/attribution reporting, and client-facing analytics. 
• Guide upstream data work when required (not “just reporting”): ingestion/landing adjustments, normalization, and curated reporting-ready outputs (data marts); vendor/custodian integration changes and schema drift handling.
• Institutionalize testing and validation for client deliverables: automated regression testing for report outputs (golden datasets, contract/schema checks, tolerance-based comparisons); data quality tests for pipeline changes (nulls, duplicates, cardinality, anomaly thresholds); repeatable pre-release validation harnesses so client outputs don’t silently regress.
• Standardize AI usage across Professional Services: create a PS “AI operating model” (prompt libraries, code review checklists, test generation patterns, estimation support); enforce human-in-the-loop controls for correctness, performance, and security; train engineers to use AI to accelerate spec-to-solution safely and consistently.
• Participate in client discussions as needed to clarify technical requirements and delivery constraints, maintaining a delivery-first mindset rather than account ownership. 
• Experience in hedge fund accounting, investment operations, or fund administration.
• Experience partnering closely with Business Analysts and implementation teams. 
• Experience scaling or operating engineering delivery within a Professional Services model. 

• 8+ years of software/data engineering experience, including leadership of delivery teams. 
• Prior experience in consulting or professional services environments with direct accountability for delivery outcomes. 
• Demonstrated ownership of estimation accuracy, scheduling, and delivery commitments. 
• Deep experience building data-centric solutions, reporting systems, exports, and client-facing analytics. 
• Strong SQL Server background and ability to lead high-stakes database work in production environments. 
• Report engineering at scale — deterministic export/report procedures with stable schemas and predictable performance. 
• Metadata-driven reporting patterns — dynamic pivots, configuration-driven layouts, and controlled client-specific variants. 
• Advanced analytical SQL — window-function–driven periodization and attribution-style computations. 
• Semi-structured + vendor-driven inputs — JSON handling and APPLY patterns to translate external schemas into normalized structures. 
• Reconciliation and correctness validation — drift detection and load validation patterns that prevent “silent wrong” outputs. 
• Performance engineering — plan-aware query design, indexing-minded implementations, and pragmatic approaches for parameter sensitivity. 
• Production-grade safety — structured failure signaling and guardrails for operationally safe reruns. 
• Practical experience applying AI tools in engineering workflows—and the maturity to standardize their use across a team: reusable prompts and scaffolding patterns, AI-assisted test case generation and validation routines, consistent PR review and “what changed / who is impacted” discipline. 
• Deep experience with financial instruments reporting—specifically accrued income treatment, syndicated loan structures, and fund accounting reporting patterns

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.