Data Analytics Engineer

About the Team 

The Data Delivery and Operations Division partners with Investment Management, Operations, Risk, and Technology to deliver trusted, timely, and analytics-ready data. 

The Data Analytics Engineering team governs investment and reference data within the analytics environment. The team transforms source data into reliable data assets that support reporting, performance measurement, risk analysis, accounting, trading, and other investment platforms. 

Position Overview 

Reporting to the Manager, Data Analytics Engineering, the Senior Data Analytics Engineer is a senior individual contributor responsible for investment data solutions across security master, entity master, reference data, pricing, holdings, and related domains. 

This role requires a strong understanding of how securities and other investment assets are identified, classified, priced, and mastered. The Senior Engineer traces information through connected systems, diagnoses why data did not flow or transform correctly, and coordinates durable solutions with business teams, engineers, Technology, and external data providers. 

The role combines investment data expertise with analytics engineering and applied statistical methods. The Senior Engineer uses SQL and Python, works within Git-based development practices, and uses established CI/CD pipelines to test and promote changes. This person must be able to learn unfamiliar tools, understand how they fit into SWIB’s data environment, and apply technology to a range of business and data problems. 

The position also provides technical guidance and mentoring to other analysts but does not have direct staff-management responsibility. 

Investment Data Mastering and Pricing 

• Serve as a subject matter expert for security master, entity master, reference data, pricing, holdings, and related investment data. 

• Interpret identifiers, classifications, instrument and issuer relationships, currencies, market conventions, corporate actions, price sources, valuation timing, and other attributes that affect investment processes. 

• Define and maintain source-selection, golden-source, and match and master rules for assigned data domains. 

Data Troubleshooting 

• Trace data from external providers through ingestion, mastering, transformation, validation, and downstream consumption. 

• Investigate securities, prices, identifiers, classifications, holdings, and other records that are missing, stale, duplicated, incorrectly mapped, or rejected. 

• Assess the business impact of data issues and coordinate resolution across Investment Management, Operations, Risk, ETL Engineering, Technology, and external providers. 

• Participate as needed in after hours on call rotation in case of critical data delivery failures 

• Identify recurring failure patterns and implement monitoring, validation, automation, or exception-handling improvements that reduce manual intervention. 

Analytics Engineering and Automation 

• Develop and optimize SQL and Python transformations, data models, reconciliations, validation routines, and analytics-ready datasets. 

• Use Git for branching, commits, pull requests, code reviews, and merge conflict resolution. 

• Use established CI/CD pipelines to execute tests, review results, promote approved changes, and validate deployments. 

• Apply peer review, automated testing, controlled deployment, observability, and documentation practices to analytics workflows. 

• Evaluate technologies and new AI capabilities based on the problem being solved and learn new tools as SWIB’s data environment evolves. 

Statistical Monitoring and Applied Data Science 

• Analyze historical patterns, distributions, relationships, and time-series behavior in investment and reference data. 

• Design rule-based and statistical monitors for missing, stale, unusual, or inconsistent securities, prices, holdings, classifications, and other data. 

• Establish thresholds and tolerances that reflect asset-class characteristics, market conditions, source behavior, and normal variation. 

• Back-test proposed controls and monitors against historical data before implementation. 

• Evaluate false positives, false negatives, detection rates, and exception volumes and adjust monitoring logic to improve its operational usefulness. 

• Evaluate advanced statistical or machine-learning techniques when they provide a measurable advantage over deterministic rules. 

• Explain statistical findings and automated alerts in practical business terms so that results remain understandable and auditable. 

Data Quality and Governance 

• Implement preventive and detective controls for timeliness, completeness, accuracy, validity, consistency, uniqueness, and referential integrity. 

• Monitor data-quality measures, investigate exceptions, perform impact analysis, and coordinate remediation. 

Solution Delivery and Technical Leadership 

• Lead complex initiatives and translate investment and operational needs into data models, transformation rules, validation requirements, test plans, and technical specifications. 

• Identify gaps in data architecture, controls, integration patterns, and support processes and recommend practical solutions. 

• Review solution designs, data models, SQL, Python, test plans, and documentation and provide clear, actionable feedback. 

• Mentor engineers in investment data, security mastering, pricing, statistical monitoring, troubleshooting, and engineering practices. 

• Lead discussions with key stakeholders across multiple business functions 

Qualifications 

• Bachelor’s degree in data analytics, data science, engineering, information systems, finance, or a related field. 

• 6+ years of progressive experience in analytics engineering, investment data management, data architecture, securities operations, or a related discipline. 

• Strong understanding of security and entity mastering, investment reference data, pricing, and how these data affect downstream investment processes. 

• Advanced SQL skills and working proficiency in Python 

• Hands-on Git experience, including branches, commits, pull requests, code reviews, and merge conflict resolution. 

• Experience with agile methodology workflow tools (Jira) 

• Experience using established CI/CD pipelines to test, promote, deploy, and validate changes. Experience designing or administering CI/CD infrastructure is not required. 

• Experience implementing data-quality controls, reconciliations, exception workflows, root-cause analysis, lineage, and governance practices. 

• Experience with cloud data platforms such as Snowflake, Microsoft Azure, or comparable technologies. 

• Ability to learn unfamiliar tools, select technology based on the problem, lead cross-functional work, and communicate with technical and investment audiences. 

Preferred Qualifications 

• Master’s degree in data science, statistics, financial mathematics, computer science, or another quantitative discipline. 

• Experience applying statistical analysis to data-quality or operational problems, including data profiling, distribution analysis, threshold design, time-series analysis, outlier detection, or anomaly detection. 

• Experience working with multiple asset classes and their reference-data and pricing conventions. 

• Experience with investment platforms or data providers such as SimCorp, Markit EDM, FactSet, Bloomberg, BlackRock Aladdin, MSCI, or Charles River Development.