Sr Analyst, Data Integration & Workflows

About the Role

Grade Level (for internal use)

11

The Team

The Senior Analyst, Data Integration & Workflows plays a critical role within the Data AI & Enablement organization, serving as a senior technical leader responsible for designing, implementing, and operationalizing production-grade data pipelines and workflow automation that power SPDJI's index and analytical solutions. This role combines hands-on technical expertise with leadership capabilities to drive delivery excellence, mentor technical talent, and ensure all data integration solutions meet enterprise standards for quality, reliability, and maintainability.

Responsibilities and Impact

Technical Leadership & Solution Delivery

• Lead complex data integration initiatives from design through production deployment, ensuring solutions are scalable, observable, and aligned with enterprise architecture standards
• Design and implement production-grade data pipelines (batch and streaming) that transform raw inputs into trusted curated outputs, incorporating robust error handling, validation, and reconciliation controls
• Establish and evangelize engineering best practices for ETL/ELT patterns, workflow orchestration, data quality controls, and operational observability across the team and value streams
• Drive technical decision-making for pipeline architecture, technology selection, and design patterns, balancing business requirements with technical feasibility and long-term maintainability
• Partner with PPD on technical planning and feasibility, providing realistic estimates, identifying technical dependencies, and shaping scope to ensure achievable delivery commitments

Enablement & Co-Development

• Lead hands-on enablement with value stream SMEs through pair programming, structured guidance, and co-development sessions—adapting approach based on SME technical capability
• Assess SME technical readiness and recommend appropriate engagement models (SME-led with review, co-development, or led build with validation)
• Build reusable automation components and templates (frameworks for ingestion, validation, transformation, publishing, backfills) that accelerate consistent delivery across domains
• Develop SME technical capabilities through targeted coaching, code reviews, and knowledge transfer, fostering a culture of engineering excellence and continuous learning
• Create and maintain technical documentation, including reference architectures, design patterns, coding standards, and implementation guides

Quality Assurance & Production Readiness

• Conduct comprehensive code reviews for SME-built and team-developed pipelines, ensuring adherence to standards for maintainability, testing, logging, data validation, and documentation
• Implement data reliability controls including validation rules, reconciliation checks, anomaly detection, and completeness/timeliness monitoring that protect downstream index processes
• Engineer observability and monitoring solutions by implementing logging standards, metrics, alerts, and runbooks that enable effective production support
• Prepare IT-ready handover artifacts including technical documentation, test evidence, operational procedures, and clear support boundaries
• Partner with IT during QA and deployment, resolving issues quickly and ensuring solutions meet enterprise standards for security, supportability, and operational excellence

Operational Excellence & Continuous Improvement

• Provide L3 support for production business-logic issues, collaborating with value stream SMEs to drive root-cause analysis and implement permanent fixes for recurring failures
• Optimize pipeline performance and cost through appropriate partitioning strategies, caching, incremental processing patterns, and compute resource tuning
• Implement workflow orchestration patterns (scheduling, dependency management, retries, idempotency, parameterization) ensuring pipelines are resilient to upstream variability
• Capture and share lessons learned, updating engineering playbooks, patterns, and standards based on production outcomes and emerging best practices
• Monitor operational metrics related to pipeline reliability, data quality, performance, and cost efficiency; drive continuous improvement initiatives

Collaboration & Stakeholder Management

• Collaborate with Data Integration Lead to shape team strategy, prioritize initiatives, and align technical approaches with organizational goals
• Partner effectively with AI Solutions and Data Governance teams on cross-cutting concerns including data quality standards, AI pipeline requirements, and compliance
• Engage with Data Value Streams to understand business requirements, validate technical solutions, and ensure alignment with domain expertise
• Work with Data Services & Strategy teams (Vendor Governance, Catalog) to establish scalable integration patterns and ensure proper metadata and lineage tracking
• Build strong relationships with IT and PPD teams to ensure infrastructure readiness, smooth deployments, and operational excellence

Shared Accountabilities

• With Data Integration Lead: Execute on team strategy; provide technical leadership on complex initiatives; mentor junior team members; contribute to standards and capability development
• With PPD: Collaborate on technical feasibility assessments and planning; provide realistic estimates; align integration efforts with platform capabilities and roadmap
• With IT: Ensure infrastructure readiness; coordinate handover processes; support production gateway requirements; partner on operational excellence
• With Data Value Streams: Co-develop solutions with SMEs; validate business logic alignment; assess and develop SME technical capabilities
• With Data Services & Strategy: Establish scalable integration patterns; ensure proper metadata and lineage tracking; align with vendor governance requirements
• With AI Solutions & Data Governance: Coordinate on data quality standards, AI data pipeline requirements, and governance compliance

Ownership

• Complex Technical Initiatives: Own the end-to-end delivery of high-complexity data integration and workflow automation projects
• Engineering Standards Implementation: Responsible for implementing and enforcing technical standards, patterns, and best practices within assigned domain or value streams
• SME Technical Development: Own the hands-on enablement and capability development of assigned value stream SMEs in data engineering practices
• Production Solution Quality: Accountable for ensuring all solutions meet production readiness criteria before IT handover

Parameters for Success

• Deliver Production-Ready Solutions: Consistently deliver high-quality, production-ready data pipelines that meet business requirements and enterprise standards
• Build SME Capability: Demonstrably improve technical capabilities of value stream SMEs through effective enablement and mentorship
• Drive Reusability: Create and promote adoption of reusable components and standardized patterns that accelerate delivery
• Ensure Operational Excellence: Implement robust observability, monitoring, and support frameworks that minimize production incidents and enable rapid issue resolution
• Foster Technical Excellence: Contribute to a culture of craftsmanship, continuous improvement, and engineering best practices

Key Performance Indicators (KPIs)

• Solution Delivery Quality: Percentage of delivered pipelines that pass IT QA on first submission; production incident rate for delivered solutions
• SME Capability Development: Measurable improvement in technical skills of mentored SMEs through assessments, code review quality progression, and feedback
• Operational Reliability: Pipeline uptime and reliability metrics; mean time to resolution for production issues; data quality incident rates

What We’re Looking For:Basic Required Qualifications

Education & Experience

• Bachelor's degree in Computer Science, Engineering, Informatio