Strategic Leadership
- Drive the roadmap for process engineering initiatives that align with broader Data Operations and enterprise objectives.
- Partner on efforts to modernize legacy workflows and build scalable, reusable solutions that support operational efficiency, risk reduction, and enhanced observability.
- Define and track success metrics for operational performance and process health across critical data pipelines.
Process Engineering Solutioning
- Design and develop tools and products to support operational efficiency, observability, risk management, and KPI tracking.
- Define success criteria for data operations in collaboration with stakeholders across teams.
- Break down complex data challenges into scalable, manageable solutions aligned with business needs.
- Proactively identify operational inefficiencies and deliver data-driven improvements.
Data Insights Visualization
- Design data science solutions to analyze vendor data trends, identify anomalies, and surface actionable insights for business users and data stewards.
- Develop and maintain dashboards (e.g., Power BI, Tableau) that provide real-time visibility into vendor data quality, usage patterns, and operational health.
- Create metrics and KPIs that measure vendor data performance, relevance, and alignment with business needs.
Quality Control Data Governance
- Build automated QC frameworks and anomaly detection models to validate data integrity across ingestion points.
- Work with data engineering and governance teams to embed robust validation rules and control checks into pipelines.
- Reduce manual oversight by building scalable, intelligent solutions that detect, report, and in some cases self-heal data issues.
Testing Quality Assurance
- Collaborate with data engineering and stewardship teams to validate data integrity throughout ETL processes.
- Lead the automation of testing frameworks for deploying new datasets or new pipelines.
Collaboration Delivery
- Work closely with internal and external stakeholders to align technical solutions with business objectives.
- Communicate effectively with both technical and non-technical teams.
- Operate in an agile environment, managing multiple priorities and ensuring timely delivery of high-quality data solutions.
Experience Education
- 8+ years of experience in data engineering, data operations, analytics, or related fields, with at least 3 years in a leadership or senior IC capacity.
- Bachelors or Master s degree in a quantitative field (Computer Science, Data Science, Statistics, Engineering, or Finance).
- Experience working with financial market data providers (e.g., Bloomberg, Refinitiv, MSCI) is highly valued.
- Proven track record of building and deploying ML models.
Technical Expertise
- Deep proficiency in SQL and Python, with hands-on experience in data visualization (Power BI, Tableau), cloud data platforms (e.g., Snowflake), and Unix-based systems.
- Exposure to modern frontend frameworks (React JS) and microservices-based architectures is a strong plus.
- Familiarity with various database systems (Relational, NoSQL, Graph) and scalable data processing techniques.
Leadership Communication Skills
- Proven ability to lead cross-functional teams and influence without authority in a global matrixed organization.
- Exceptional communication skills, with a track record of presenting complex technical topics to senior stakeholders and non-technical audiences.
- Strong organizational and prioritization skills, with a results-oriented mindset and experience in agile project delivery.
Preferred Qualifications
- Certification in Snowflake or equivalent cloud data platforms
- Certification in Power BI or other analytics tools
- Experience leading Agile teams and driving enterprise-level transformation initiatives
