Job Summary
The Data Engineer and Analytics Specialist delivers enterprise data engineering, business intelligence, and analytics solutions across multiple projects and hybrid or cloud environments. This role designs and operates data pipelines, warehouses, lakehouses, dimensional models, dashboards, and analytical solutions using technologies such as Python, PySpark, SQL, Azure, Databricks, Microsoft Fabric, Power BI, and related platforms. The position translates business requirements into reliable data and analytics products, communicates insights to stakeholders, and supports modernization, automation, and informed decision-making.

Key Responsibilities
• Design, build, maintain, and optimize scalable data pipelines across on-premises, hybrid, and cloud platforms, including Azure, Databricks, Microsoft Fabric, GCP, and AWS.
• Develop and support data warehouse and lakehouse solutions using star schemas, snowflake schemas, fact tables, dimension tables, data marts, and other enterprise data models.
• Plan, execute, validate, and support data migration and modernization initiatives across on-premises, cloud, and cross-database environments.
• Develop end-to-end ETL/ELT workflows using Python, PySpark, SQL, SSIS, Azure Data Factory, Fabric Data Factory, Dataflows, notebooks, and other data integration tools.
• Implement data quality validation, error handling, logging, monitoring, scheduling, performance tuning, security controls, and access management for reliable data operations.
• Create executive dashboards, KPI reports, self-service BI solutions, and interactive Power BI reports using DAX and appropriate visualization techniques.
• Analyze trends, patterns, anomalies, and business performance using Python, R, statistical methods, predictive models, and AI-enabled techniques, and communicate findings to technical and non-technical audiences.

Required Qualifications
• Minimum 9 years of relevant experience.
• Minimum 4 years of experience building executive dashboards, KPI reporting, self-service business intelligence solutions, and business performance reports.
• Minimum 4 years of experience working with cloud modernization initiatives or hybrid and cloud data platform implementations.
• Minimum 2 years of experience planning, executing, validating, and supporting data migrations across on-premises, cloud, and cross-database environments.
• Minimum 4 years of experience designing enterprise data warehouses or lakehouses using star schemas, snowflake schemas, and fact-and-dimension modeling.
• Minimum 5 years of experience working as a Data Engineer and/or Data Analyst.
• Minimum 5 years of experience using Python, including PySpark, and SQL to develop, orchestrate, optimize, and troubleshoot enterprise-grade ETL/ELT workflows.
• Minimum 4 years of experience designing and implementing ETL processes and data pipelines that transform and load data from multiple sources into data warehouses or lakehouse environments.

Preferred Qualifications
• Minimum 1 year of experience using AI-assisted development tools to improve productivity, code quality, documentation, testing, and data engineering workflows, with appropriate review and quality controls.
• Minimum 2 years of experience designing, implementing, or maintaining DevOps, CI/CD, and Infrastructure as Code practices for automated deployment and management of cloud-based data platforms.
• Experience with modern data technologies such as Microsoft Fabric, Databricks, Spark, and Delta Lake, as well as supporting enterprise-scale applications in public-sector or mixed-delivery environments.