Data Engineer

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: Data Engineering Pipeline Management: 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