Data Warehouse Engineer

Maverick Payments is a family-owned and privately held full-service payment provider. The Data Warehouse Engineer is responsible for designing, building, and operating Maverick’s Azure data platform, ensuring operational excellence and developing new data pipelines to support analytics and reporting.

Responsibilities:

• 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 (Change Data Capture), 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 cross‑functional analytics
• Evaluate emerging Azure services and tooling for platform improvement
• Other duties as assigned

Requirements:

• 3+ years in data engineering/data warehousing or related technical roles
• Advanced SQL/T‑SQL: complex queries, stored procedures, views, indexing, and performance tuning
• Hands‑on Azure: Data Factory, Synapse SQL, Data Lake Gen2, Key Vault (or strong equivalents: Snowflake, BigQuery, Redshift)
• Proven delivery of ETL/ELT for file‑based (CSV/JSON/Parquet) and API data sources
• Strong data modeling: star/snowflake, dimensional modeling, fact/dimension tables, SCDs (Type 1/2)
• Git proficiency and CI/CD concepts for infra/pipeline deployment (Azure DevOps/GitHub Actions; ARM/Bicep/Terraform a plus)
• Demonstrated troubleshooting and RCA across complex systems; able to drive solutions independently
• Experience supporting BI/analytics consumers (Power BI preferred; semantic models, incremental refresh, gateway basics)
• Advanced SQL/T‑SQL; performance tuning; query optimization; indexing; statistics; partitioning; workload management
• Azure Data Factory; pipeline orchestration; triggers; parameterized reusable templates; error handling; retries; idempotency
• Azure Synapse (dedicated/serverless SQL pools); PolyBase/COPY INTO; distribution strategies (hash/round‑robin/replicate)
• Azure Data Lake Gen2; Parquet/Delta; hierarchical folder structures; retention/archival; lifecycle management
• Incremental loads; CDC; SCD Type 2; watermarking; late‑arriving data; schema evolution
• Data modeling (Kimball); dimensional schemas; semantic layers; Power BI model optimization
• Observability & reliability: Azure Monitor, Log Analytics, KQL, alerts, dashboards; SLA/SLO adherence, post‑mortems
• Security & governance: RBAC/ACLs, Key Vault, Managed Identity, Private Endpoints/Virtual Networks, data masking, tokenization, Purview lineage/catalog
• DevOps: Git, pull requests, code reviews; CI/CD (Azure DevOps/GitHub Actions); IaC (ARM/Bicep/Terraform)
• Cross‑functional communication; clear documentation; vendor collaboration; prioritization by business impact; ownership mindset
• Power BI preferred; semantic models, incremental refresh, gateway basics
• Pursue DP‑203, DP‑300 (preferred)