Role Description We are looking for an experienced Data Engineer to join a growing data and analytics team focused on building modern, cloud-based data solutions. This role will be responsible for designing, developing, and optimizing scalable data platforms that support enterprise reporting, analytics, and data-driven decision making. The ideal candidate has hands-on experience with Microsoft's Azure Data Platform and a strong understanding of data engineering best practices, including data governance, data quality, and data management. Key Responsibilities - Design, develop, and maintain scalable data pipelines and ETL/ELT processes. - Build and optimize data solutions using Azure cloud technologies. - Develop data ingestion, transformation, and integration processes from multiple data sources. - Create and maintain data models that support reporting, analytics, and business intelligence initiatives. - Develop reusable and high-performance data workflows using SQL and Python. - Collaborate with Data Analysts, Data Scientists, Software Engineers, and business stakeholders to translate requirements into technical solutions. - Enhance data architecture through automation, optimization, and performance improvements. - Support CI/CD processes and deployment pipelines for data engineering solutions. - Build and maintain datasets that support enterprise reporting and analytics platforms. - Troubleshoot production data issues and perform root cause analysis. - Participate in code reviews and contribute to engineering best practices. Qualifications - Strong experience with SQL development. - Experience building and maintaining enterprise-scale data pipelines. - Hands-on experience with Microsoft Azure (MUST). - Experience with Azure Synapse Analytics. - Experience with Azure Databricks. - Experience with Azure Data Factory. - Experience with Azure Data Lake. - Strong Python programming skills. - Experience with source control and CI/CD methodologies. - Experience working with large datasets and distributed data processing. - Understanding of data modeling and modern data architecture principles. - Experience with data integration and transformation processes. - Strong analytical and problem-solving skills. - Excellent communication and collaboration abilities. - Experience with Azure cloud services and data platform technologies. - Experience with Azure Kubernetes Service (AKS). - Experience with Kubernetes. - Experience using Ansible for infrastructure automation and configuration management. - Experience creating and maintaining technical documentation using Markdown. - Experience with Azure DevOps (ADO) for source control, work item management, and CI/CD pipelines. - Experience with GitHub and Git-based development workflows. - Experience with Microsoft Power Platform (Power BI, Power Apps, Power Automate, and Power Pages). - Experience with Ontology Engineering and semantic data modeling. - Experience writing queries using SPARQL. - Experience writing queries using Cypher. - Experience developing and consuming GraphQL APIs. - Experience designing and working with graph databases and knowledge graph solutions. Preferred Qualifications - Experience with Power BI datasets and reporting solutions. - Experience with data governance frameworks and data quality initiatives. - Understanding of enterprise data management practices. - Exposure to Big Data technologies. - Experience working within Agile environments. - Ability to collaborate effectively with both technical and business teams.

• Lead the design and evolution of the data ecosystem supporting advanced data forensics, fraud detection, investigative analytics, and threat intelligence • Establish the architecture, governance, and technical foundations necessary to deliver scalable analytics, machine learning, and investigative intelligence capabilities • Design and maintain enterprise-scale data architectures supporting forensic analytics and security operations • Develop target-state architectures that support both operational reporting and advanced analytical workloads • Architect and optimize cloud-based analytics platforms utilizing Azure and Databricks technologies • Implement enterprise data governance standards and controls • Build and maintain architectures that support machine learning, artificial intelligence, and advanced analytics workloads.

• Build advanced data pipelines utilizing the Medallion Architecture to create high-quality data sources in Snowflake. • Leverage AI-assisted development tools to reduce manual pipeline work and explore emerging Snowflake capabilities like Cortex to support AI/ML-enabled workflows. • Build Data Applications that contribute to interactive experiences, including Streamlit-based apps, transforming Snowflake from a warehouse into a full-service data platform. • Create robust data pipelines using AWS technologies to serve specific business needs. • Design and evolve analytical data models that support BI, advanced analytics, and AI workloads. • Partner directly with Analytics and Data Science teams to translate complex business questions into well-structured, modular data assets. • Write complex Python/SQL, scripts, and stored procedures to ensure 99.95% uptime and optimize query performance for downstream consumption. • Serve as a primary advisor to the Data Management team to identify opportunities for technical improvements and automation. • Act as a leader for junior and mid-level engineers, providing direction on programming best practices, query optimization, and business tact.

• Pipeline Design and Development; Architects and builds scalable data pipelines using modern ETL (Extract, Load, Transform) tools and frameworks such as DBT (Data Build Tool), or similar. • Automates data ingestion processes from various sources including databases, APIs, and third party services. • Data Storage and Management - Designs and implements data warehousing solutions using platforms like Snowflake, Redshift, or BigQuery. • Optimizes storage solutions for performance, cost efficiency, and scalability. • Data Modeling - Develops and maintains logical and physical data models to support business analytics. • Creates and manages dimensional models, star/snowflake schemas, and other data structures. • Data Transformation - Transforms raw data into clean, organized, and analytics-ready datasets using SQL, Python, or other relevant languages. • Implements data transformation workflows to handle data cleansing, normalization, and enrichment. • Data Quality Assurance - Conducts data validation and consistency checks to ensure the accuracy and reliability of data. Implements data quality monitoring and alerting mechanisms. • Cross-Functional Collaboration - Works closely with data analysts, data scientists, and business stakeholders to gather requirements and understand their data needs. • Acts as a liaison between technical teams and business units to translate business requirements into technical specifications. • Governance Policies Implements and enforces data governance policies to ensure data privacy, security, and compliance with relevant regulations. • Defines and manages data access, controls, permissions, and audit trails. • Security Measures - Monitors and enforces data security measures to protect sensitive information from unauthorized access and breaches. • Ensures compliance with industry standards and regulations such as GDPR, CCPA, or HIPAA and others as applicable.