Job Summary
Seeking a Senior Data Engineer to design and build client's data warehouse and analytics platform that powers embedded, self-service analytics within our SaaS product.
This role is focused on enabling customers to safely and intuitively access their data through dashboards, reports, and interactive analytics—without relying on external BI tools or custom extracts.
You will own the architecture and implementation of the data warehouse, ensuring it supports high-performance, multi-tenant analytics, strong governance, and future AI-driven capabilities.
Key Responsibilities
Embedded Analytics & Data Platform
Architect and implement a data warehouse optimized for embedded analytics in a multi-tenant SaaS environment.
Design data models that are intuitive, well-documented, and suitable for customer self-service analytics.
Support interactive dashboards, parameterized reports, and ad-hoc exploration directly within the client application.
Ensure low-latency query performance and predictable behavior for customer-facing analytics.
Data Modeling & Customer Semantics
Define and maintain business-friendly semantic layers that abstract complex scheduling logic into understandable metrics and dimensions.
Standardize definitions for key measures (e.g., staffing levels, overtime hours, compliance exceptions).
Balance flexibility with guardrails to prevent misuse or misinterpretation of data by end users.
Collaborate with product and UX teams to ensure data structures align with customer workflows.
Data Pipelines & Reliability
Build and operate reliable ETL/ELT pipelines that transform operational scheduling data into analytics-ready datasets.
Manage incremental loads, historical snapshots, and slowly changing dimensions.
Implement data validation, freshness monitoring, and automated alerting.
Support both batch and near-real-time analytics use cases as required.
Multi-Tenancy, Security & Governance
Design data isolation strategies to ensure strict tenant separation and secure access controls.
Implement role-based access and row-level security to support varied customer permissions.
Define data retention and auditability standards aligned with public-sector expectations.
Ensure compliance with internal security policies and customer contractual requirements.
AI & Advanced Analytics Readiness
Ensure the data platform supports AI and machine-learning requirements, including feature extraction, historical datasets, and labeled data.
Enable future use cases such as forecasting, anomaly detection, and intelligent scheduling recommendations.
Expose analytics datasets that can be reused for model training and inference.
AI Platform Feature Intelligence
Translate operational workflows (scheduling, coverage, OT, fatigue, leave) into machine-learnable representations, ensuring models reflect real-world public safety constraints and decision logic.
Lay the groundwork for AI-driven in-product experiences, including conversational analytics, predictive insights, and recommendation surfaces embedded directly into core scheduling workflows.
Collaboration & Platform Evolution
Partner with application engineers to integrate analytics seamlessly into the InTime UI.
Work closely with product management to prioritize analytics features that drive customer value.
Document data models, metrics, and best practices for internal and external consumption.
Help define long-term data platform strategy and technical standards.
Required Qualifications
7+ years of experience in data engineering, analytics engineering, or similar roles.
Proven experience designing data warehouses that power customer-facing analytics.
Advanced SQL skills and hands-on experience with modern data warehouses (Snowflake, BigQuery, Redshift, Databricks).
Strong proficiency in Python for data transformation and pipeline development.
Experience supporting multi-tenant SaaS data architectures.
Solid understanding of data modeling, performance optimization, and cost management.
Experience with embedded analytics platforms or semantic layers.
Familiarity with row-level security, tenant isolation, and analytics governance.
Exposure to AI/ML data requirements or feature-store concepts.
Experience with time-series or operational analytics.
Background in workforce management, scheduling, or public-sector software is desirable.
