Senior Manager, SQL Development
@ SiepeSenior Manager, SQL Development
This job is still taking applications, but it's been up a while.
About the job
Siepe is a fast-growing tech company in Dallas focused on software and data solutions for investment managers. We modernize finance with real-time insights, rewarding curiosity and initiative. This role leads client solutions, data projects, and AI standardization, offering impactful work and growth.
Requirements
- 8+ years software/data engineering
- Leadership of delivery teams
- Deep experience with SQL Server
- Building data-centric solutions
- AI tools in workflows
Qualifications
- Bachelor's degree or higher
- Experience in professional services
- Strong estimation and scheduling skills
- Financial services background
- Authorization to work
Full job description
What You’ll Be Doing
- 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.
What You’ll Bring
- 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
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