Analytics Engineer

• Design and develop data serving and semantic layers to support business needs ranging from traditional reporting to reverse ETL and LLM/AI implementations.
• Develop data models for use by analysts and applications from across the business using modern dimensional modeling practices to efficiently serve accurate, consistent data.
• Build and maintain robust, scalable data pipelines to extract, transform, and load (ETL, ELT) data from various sources into the data platform using both batch processing and event-driven patterns.
• Create efficient, performant data models to support business requirements and analytics needs.
• Implement and automate data quality checks and monitoring to ensure data accuracy and drive observability.
• Manage, troubleshoot and optimize existing data infrastructure, including cloud-based solutions and on-premises systems.
• Leverage cloud data platforms (Fabric, Databricks, Snowflake, etc.) for data storage, processing, and analysis.
• Work closely with data analysts, data scientists, and business stakeholders to understand their data needs and deliver solutions.
• Identify and resolve data-related issues and challenges for technical and non-technical stakeholders.
• Support company-wide projects ranging from server migrations to mergers and acquisitions as a data and systems expert.
• Automate data processes to improve efficiency and reduce manual effort.
• Implement modern concepts like metadata-driven pipelines and Infrastructure-as-Code that will play a key role in a scalable, resilient data platform.
• Document data processes, architecture, and workflows.

• Collaborate with stakeholders to understand business requirements and translate them into scalable and efficient data analytics solutions.
• Possess in-depth knowledge and hands-on experience with Microsoft Fabric ecosystem (Lakehouse, PySpark/SQL notebooks, data pipelines, and deployment pipelines).
• Understanding Microsoft Dynamics as a data source a big plus.
• Design data models that bridge the Fabric Lakehouse, Power BI semantic models, and reporting layers, ensuring consistency between raw, curated, and business-facing data.
• Optimize data models to support efficient data storage, retrieval, and analysis, ensuring data integrity, accuracy, and reliability.
• Knowledge of Fabric capacity (CU) optimization a big plus.
• Implement data integration pipelines using Microsoft Fabric and other relevant tools to extract, transform, and load data from various sources into a unified data platform.
• Create visually appealing and interactive dashboards, reports, and visualizations using Microsoft Power BI and DAX language, enabling users to gain valuable insights from data.
• Identify performance bottlenecks and implement optimizations to improve data processing, query performance, and overall system efficiency.
• Collaborate with cross-functional teams, providing technical leadership and guidance to data engineers, analysts, and other stakeholders to ensure successful project delivery.
• Keep abreast of the latest advancements in data analytics technologies (Microsoft based and others) and industry trends, continuously enhancing your expertise and applying best practices in solution design and implementation.

Data Analytics Engineer

• Architect, Design and implement ETL processes.
• Develop and maintain integration tools and utilities.
• Proficient in writing SQL queries and troubleshooting issues.
• Support existing applications.
• Provide guidance and support to Junior/Mid-level developers.
• Instruct, direct, and check the work of others.
• Lead, design, code, test and debug complex development efforts.
• Document, enforce and model best standards and practices to others.
• Oversee project completions and ensure user satisfaction.
• Complies with Company policies, procedures, regulatory requirements, and complete mandatory company compliance training by prescribed deadlines.
• Ability to accept guidance and supervision from management.
• Other duties that may be reasonably assigned.

Senior ETL Developer

• Develop and maintain ETL processes to support data ingestion, transformation, and integration.
• Work with various file formats (TXT, CSV, XML, JSON, YAML) to ensure seamless data processing.
• Implement and optimize ETL workflows using EasyMorph, Databricks, Azure Data Factory, Talend, and Apache Nifi.
• Write clean, efficient, and maintainable code using Python, C#, or Java.
• Ensure data quality, accuracy, and integrity across ETL pipelines.
• Collaborate with data engineers and analysts to define and refine ETL requirements.
• Troubleshoot and resolve ETL failures, ensuring minimal downtime and impact.
• Follow test-driven development (TDD) methodologies to improve reliability and maintainability.
• Maintain and document ETL processes, providing insights and recommendations for improvements.

Associate ETL Engineer

• Lead the end-to-end delivery of complex data engineering projects, including requirements definition with business stakeholders, technical design, implementation oversight, and delivery coordination with data engineering, analytics, and AI/ML teams
• Serve as the primary technical liaison between business partners and engineering teams, translating business requirements into well-scoped, executable data initiatives with clear outcomes
• Architect and develop cloud-native data solutions on AWS, including the design and deployment of data pipelines using AWS Glue, Airflow (Amazon MWAA), Step Functions, Lambda, and related services
• Lead the migration and modernization of existing data workflows, including the conversion of RPA-based automation into cloud-native orchestration solutions using AWS Airflow
• Design, develop, and deploy RESTful and authenticated APIs on Kubernetes (K8s) to expose data services and other reusable data products consumed by downstream systems
• Oversee and contribute to infrastructure-as-code practices using Terraform, ensuring analytics and data engineering workloads are provisioned, managed, and repeatable across environments
• Establish and enforce data pipeline best practices including data quality checks, lineage tracking, and governance standards across all projects under this role's scope
• Monitor, observe, and maintain production data systems and pipelines, using tools such as CloudWatch, DataDog, or Grafana as applicable
• Conduct exploratory and operational data analysis to surface deficiencies, validate pipeline outputs, and support data-driven decision making for business stakeholders
• Define and communicate data architecture standards and patterns to both technical and non-technical audiences, including documentation of architectural decisions and pipeline design
• Collaborate on cross-cutting concerns such as data security, infrastructure alignment, and platform strategy
• Coordinate with DevOps and infrastructure teams to ensure that data engineering deliverables align with CI/CD pipelines, containerization standards, and broader organizational engineering practices

Principal Data Analytics Engineer

• Support the design, development, and maintenance of data platforms
• Configure and maintain data warehouse / lakehouse and related code base and schemas for the firm, including integrations with Finance, HR, Marketing, and other firm-related database applications
• Develop, document, and maintain key data integration processes in support of projects and ongoing services
• Provide data integration services in support of local and hosted environments for enterprise applications including the use of APIs and other connection methods
• Design, implement and maintain scalable and reliable data pipelines, data quality and transformation processes, and databases to support a variety of data and analytics needs
• Design and implement scalable and secure data processing pipelines using Azure Data Factory, Azure Databricks, and other Azure services
• Manage and optimize data storage using Azure Data Lake Storage and Azure SQL Data Warehouse
• Develop data models and maintain data architecture to support data analytics and business intelligence reporting
• Ensure data quality and consistency through data cleaning, transformation, and integration processes
• Proactive monitoring and troubleshooting data-related issues within the Azure environment to maintain high availability and performance
• Collaborate with data scientists, business analysts, and other stakeholders to understand data requirements and implement appropriate data solutions
• Implement data security measures, including encryption, access controls, and auditing, to protect sensitive information
• Automate data pipelines and workflows to streamline data ingestion, processing, and distribution tasks
• Support the appropriate sourcing, evaluation, selection, and utilization of data engineering tools, particularly emerging artificial intelligence-enabled products
• Support the custom development and implementation of proprietary Machine Learning, LLM and AI enabled products, including the selection and engagement of solutions partners, development of grounding, RAG, vectorizing, and related technical protocols and procedures
• Support to use-case evaluation and product design and implementation
• Optimize data processing systems to enhance performance, improve reliability and optimize cost
• Participate in cross-functional relationships with software engineers, solutions engineers, and other business stakeholders to collaboratively create solutions, development standards, and support business processes
• Partner with the broader Data Strategy team in the development and maintenance of a comprehensive and up-to-date data catalog by overseeing the capture of end-to-end data flows and the creation of source-to-target documentation, ensuring a detailed and accessible repository that enhances data understanding and transparency across the organization
• Support a culture of excellence in data transformations and clear schema design, ensuring alignment with operational and analytical business requirements
• Ensure our data architecture is secure and compliant per company policies and industry standards
• Support information and data governance roles in applying appropriate firm policies
• Collaborate among a team of data engineers and liaise closely with data solutions analysts and database administrators with a strong focus on high-quality solution delivery and team culture
• Partner with IT staff, vendors, and other third parties to address system-level technical issues, manage internal and external integrations, and implement upgrades and enhancements
• Support data and intelligence solutions teams as we establish COE and best practices for analytics and data dashboard and application design and development
• Develop standard metrics reporting and analyze trends to measure the quality and quantity of data maintained within the data platform
• Contribute to standard configuration documentation and related guides for data consumers
• Collaborate to design end-user training, onboarding, and user adoption programs and communications
• Special projects and duties as assigned

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