Data Warehouse Engineer

Exciting Opportunities at Maverick Payments
! Join our fast‑paced, growing company and further your career with Maverick Payments
, where you can thrive, your ideas are valued, and your colleagues feel more like family than coworkers!

Maverick Payments is a family‑owned and privately held full‑service payment provider. Located in Calabasas, California, Maverick has created innovative technology designed for sales organizations, such as ISOs and ISVs, looking to monetize payments by reselling our white‑labeled payments stack. Our payment products include merchant acquiring services, a proprietary gateway, ACH processing, fraud & chargeback tools, analytics, and other value‑add services. Maverick’s team includes underwriting, risk management, compliance & legal, technology & product development, on‑boarding, customer support, information technology, and more.

The Data Warehouse Engineer is a hands‑on role responsible for designing, building, and operating Maverick’s Azure data platform. Reporting to the Data Team Director, you will partner with external implementation resources in Phase 1 and then own daily operations, reliability, and continuous improvement of the platform. The role balances operational excellence (pipeline uptime, SLAs, observability) with platform development (new data pipelines, sponsor bank integrations, performance/cost optimization).

Your work enables the Maverick organization to access reliable, timely, and governed data for analytics, BI, and regulatory reporting.

• Monitor health and SLAs via Azure Monitor/Log Analytics, Synapse and Data Factory dashboards; respond to critical pipeline incidents within 2 hours.
• Triage and resolve data integration failures (sponsor bank files, CRM/ITSM connectors, API ingestions), distinguishing data quality vs. pipeline vs. infrastructure issues; escalate when needed.
• Perform root‑cause analysis (RCA) and drive corrective actions; maintain and conduct post‑mortems.
• Support users with ad‑hoc SQL, Power BI connectivity, refresh scheduling, and semantic model best practices.
• Track pipeline performance and data freshness, optimize query runtimes, memory, and SLA compliance.
• Design and build Azure Data Factory pipelines (ETL/ELT) for batch and incremental ingestion using parameterized, reusable templates.
• Write and optimize T‑SQL in Synapse SQL pools for reliability and maintainability.
• Implement incremental loads, CDC, idempotent retries, and robust error handling.
• Onboard new sponsor banks and data sources against the Director’s roadmap; standardize patterns for file‑based (CSV/JSON/Parquet/Delta) and API‑based data.
• Strengthen data quality and observability with validation checks, reconciliations, and anomaly detection.
• Optimize Synapse cost and performance: distribution strategies (hash/round‑robin/replicated), partitioning, indexing and statistics, workload management, scale up/down and pause/resume patterns, and archival/retention strategies.
• Collaborate with vendors during Phase 1; participate in code reviews, document architecture decisions (ADRs) and platform standards.
• Integrate Microsoft Purview for data lineage/catalog for transformation governance where appropriate.
• Create and maintain runbooks (“respond to X failure”), troubleshooting guides, and common query patterns.
• Document pipeline architecture, data lineage, schemas, and transformation logic for knowledge transfer.
• Maintain a data dictionary/metadata (descriptions, refresh cadence, PII tags, sensitivity labels).
• Train internal stakeholders on basic SQL and data request procedures; contribute to architecture diagrams.
• Assist with PCI‑DSS and SOC 2 reviews (encryption at rest/in transit, RBAC/ACLs, Key Vault, Private Link, Managed Identity, least privilege).
• Stay current on Azure/Synapse best practices, security updates, and cost optimization.
• Attend vendor training in Phase 1; pursue DP‑203, DP‑300 (preferred).
• Share knowledge with the Director and future data science hires via code examples, patterns, and mentoring.
• Deliver specialized data engineering enhancements as directed by the Data Team Director.
• Support ad‑hoc analysis, process improvements, and…