This role provides the opportunity to work at the intersection of data engineering and analytics, making a tangible impact on business strategy and operations. You will work with cutting-edge tools, collaborate with diverse teams, and help shape the data strategy across the organization while solving challenging, high-impact problems.
Responsibilities:
DataEngineeringPipelineManagement:
- Design, develop, and maintain scalable ETL pipelines for ingestion, transformation, and integration of structured and unstructured
- Optimize SQL queries, stored procedures, triggers, and automation scripts to handle large, complex datasets
- Ensure data quality, integrity, and consistency across multiple systems and
- Implement error handling, logging, monitoring, and alerting mechanisms for reliable production
- Collaborate with DevOps and engineering teams to deploy data workflows and ensure seamless integration with production
Data Modeling Governance:
- Design and implement relational, dimensional, and Master Data Management (MDM) models tailored to finance, operational, and enterprise
- Develop detailed entity-relationship (ER) diagrams and define indexing, partitioning, and normalization strategies for optimal
- Establish and enforce data governance, validation rules, standardization policies, and documentation for maintainable
- Implement slowly changing dimensions (SCDs) and hierarchical structures to support evolving business requirements.
Analytics Business Impact:
- Translate raw operational, financial, and transactional data into actionable insights that drive revenue growth, cost reduction, and process optimization.
- Develop predictive and prescriptive models for forecasting, anomaly detection, risk assessment, and decision support.
- Validate, monitor, and improve model accuracy, fairness, explainability, and operational
- Quantify the business impact of data initiatives and communicate actionable recommendations to stakeholders.
Collaboration Communication:
- Partner with business intelligence, analytics, finance, operations, and product teams to identify opportunities for data-driven
- Produce dashboards, reports, and narratives that clearly communicate insights to non-technical stakeholders.
- Translate complex technical concepts into business-relevant
- Document data models, transformation logic, metadata, and processes for cross-team knowledge sharing.
Data Quality Responsible AI:
- Profile and monitor data to detect inconsistencies, gaps, and anomalies, implementing corrections as
- Enforce metadata standards, data lineage, and data security, privacy, and compliance best practices.
- Ensure ethical use of AI/ML models by managing bias, transparency, and operational
- Maintain rigorous documentation of assumptions, limitations, and model performance for auditing and
Required Skills Experience:
- Strong SQL programming (PL/SQL, T-SQL) with hands-on experience in relational databases such as Oracle, SQL Server, and
- Expertise in data modeling (relational, dimensional, MDM) and database design best
- Proven experience with ETL tools such as Informatica, Talend, or
- Solid understanding of data governance, lineage, and quality
- Experience in performance tuning, indexing, partitioning, and optimization for large
- Strong problem-solving, analytical, and critical-thinking
- Ability to work effectively in cross-functional teams and communicate complex technical concepts to business stakeholders.
Preferred Qualifications:
- Experience in financial data management, enterprise MDM projects, or similar
- Familiarity with reporting and BI tools such as Power BI, SSRS, or
- Knowledge of scripting and automation using Python, Shell, or similar
- Experience deploying predictive or prescriptive models in production
- Familiarity with cloud platforms (AWS, Azure, GCP) and modern data
Desired Skills:
- Systems Analysis
- Complex Problem Solving
- Programming/configuration
- Critical Thinking
- Time Management