Key Responsibilities

Data Platform Strategy & Technical Leadership

• Define and drive the data engineering strategy and roadmap aligned with product, analytics, and enterprise architecture objectives.
• Own the technical vision and target architecture for data platforms (lakehouse, warehouse, streaming, and eventdriven patterns).
• Make and govern architecture decisions, balancing scalability, cost, performance, security, and regulatory requirements.
• Act as the design authority for complex data pipelines and platforms, reviewing and approving solution designs.

Advanced Data Platform & Pipeline Engineering

• Oversee the design and implementation of enterprisescale batch and streaming data pipelines, using Python and modern data frameworks.
• Ensure robust ingestion, transformation, and serving layers across internal and external data sources (APIs, databases, files, events).
• Drive the development of reusable data engineering frameworks, accelerators, and reference implementations.
• Establish and enforce data contracts, schemas, versioning strategies, and documentation standards across teams.

Data Quality, Reliability & Operational Excellence

• Set and own data quality, reliability, and observability standards across all managed data products.
• Define and govern SLAs/SLOs for critical data assets, ensuring businesscritical use cases are protected.
• Lead major incident management and rootcause analysis, ensuring durable, systemic fixes rather than tactical workarounds.
• Embed testing strategies (unit, integration, data quality, regression) as nonnegotiable engineering standards.

Performance, Scalability & Cost Governance

• Ensure platforms and pipelines are designed for scale, resilience, and fault tolerance (idempotency, retries, checkpointing, backpressure).
• Drive continuous optimization of compute usage, storage layouts, query performance, and costs.
• Make datadriven tradeoff decisions between performance, cost, complexity, and maintainability.

People Leadership & Team Enablement

• Provide technical leadership, mentoring, and coaching to Data Engineers across multiple teams or initiatives.
• Set clear expectations for engineering quality, delivery discipline, and professional development.
• Support capacity planning, skill development, and succession planning within the data engineering capability.
• Act as an escalation point for complex technical and delivery challenges.

CrossFunctional & Stakeholder Collaboration

• Partner closely with Product, Architecture, Backend Engineering, Analytics, and Data Science leads to translate strategic objectives into executable data solutions.
• Engage senior stakeholders to explain technical tradeoffs, risks, and investment needs in clear, businessaligned language.
• Ensure data platform decisions support downstream consumption patterns (APIs, analytics, ML, operational use cases).

Security, Privacy, Risk & Compliance Ownership

• Own the implementation and governance of securebydesign data engineering practices (access controls, encryption, secrets management).
• Ensure platforms comply with enterprise, regulatory, and privacy obligations (data classification, lineage, retention, auditability).
• Act as a key contributor to audits, risk assessments, and data governance forums, representing the data engineering domain.

Operations, Governance & Continuous Improvement

• Hold accountability for production data platforms, ensuring operational stability, availability, and ongoing improvement.
• Reduce operational risk and toil through automation, standardization, and platformlevel capabilities.
• Define, maintain, and evolve data engineering standards, guardrails, and best practices across the Digital Factory.
• Contribute to broader technology governance and platform strategy at program or portfolio level.