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 continuous improvement of the platform.

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
• 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
• DP-203, DP-300 (preferred)
• Power BI preferred; semantic models, incremental refresh, gateway basics