Data Engineer, Vice President

Senior data engineering role delivering large-scale data integration solutions across CRM ecosystems using ETL tooling, Python automation, and advanced SQL/No

SQL capabilities.

The Vice President, Technology (Data Engineer) will lead the design and delivery of enterprise‑grade data integration solutions supporting CRM and Analytics platforms. This role requires deep hands‑on expertise in ETL/ELT engineering, Python scripting for automation and data loads, and exceptional proficiency across both relational (SQL) and non‑relational (No

SQL) data technologies. The successful candidate will partner with architecture, application, and business teams to build reliable, secure, and scalable pipelines that enable analytics, operational reporting, and downstream system integration.

• Data integration delivery: Design, build, and operate robust batch and near‑real‑time integration pipelines for CRM data domains (e.g., customers, products, orders, invoices, service interactions).
• ETL/ELT engineering: Develop and optimize workflows using enterprise ETL tools; establish reusable patterns for ingestion, transformation, and publish layers.
• Python engineering: Build Python‑based utilities and frameworks for data loads, automation, validation, reconciliation, and operational tooling.
• SQL excellence: Write and tune complex SQL (query optimization, indexing strategy awareness, incremental loads, CDC patterns, and performance troubleshooting).
• No
  
  SQL competence: Apply non‑relational data modeling and query patterns where appropriate (document/columnar/key‑value/graph), including performance and consistency considerations.
• Data quality and controls: Implement validation rules, error handling, auditability, and reconciliation controls; create monitoring/alerting to meet SLAs/SLOs.
• Architecture and design: Contribute to system architecture and integration design decisions (data contracts, schemas, idempotency, versioning, resiliency).
• Security and compliance: Ensure data pipelines follow security best practices (encryption, access control, secrets management) and align with retention and privacy requirements.
• Collaboration: Work closely with CRM application teams, enterprise architects, and analytics consumers to align on data definitions and delivery priorities.
• Operational ownership: Participate in release planning, production support, incident triage, and continuous improvement to enhance reliability and reduce run‑time cost.

• 6-10 years of experience in data engineering, data integration, or platform engineering with a focus on large enterprise programs.
• Proven track record delivering data integration for ERP and/or CRM platforms in complex environments.
• Strong hands‑on experience with one or more enterprise ETL/ELT tools (e.g., Informatica, Data Stage, SSIS, Talend, Azure Data Factory, Matillion, dbt or similar).
• Advanced Python skills for data processing, automation, and scripting (packaging, logging, error handling, and testing discipline).
• Expert‑level SQL skills, including performance tuning and data warehousing concepts (dimensional modeling awareness a plus).
• Working knowledge of No
  
  SQL concepts and at least one No
  
  SQL technology (implementation experience preferred).
• Strong understanding of data pipeline fundamentals: incremental loading, late‑arriving data, schema evolution, and end‑to‑end observability.
• Ability to communicate clearly with both technical and non‑technical stakeholders; strong documentation and design skills.

• Experience with streaming/event‑driven data integration (e.g., Kafka or similar) and CDC‑based ingestion patterns.
• Experience with cloud data platforms and modern lakehouse/warehouse ecosystems.
• Knowledge of data governance practices (metadata, lineage, data quality frameworks).
• Experience implementing Dev Ops practices for data engineering (CI/CD for pipelines, infrastructure‑as‑code, automated testing).

• Engineering rigor: Writes maintainable, testable code and designs resilient systems.
• Problem solving: Diagnoses data issues quickly and drives root‑cause resolution.
• Ownership mindset: Takes accountability…