Analytics Engineer

• Support the data and analytics team in transforming raw data into meaningful insights
• Assist in building and maintaining data pipelines under guidance from senior team members
• Support data cleaning, transformation, and validation processes
• Help document data sources, transformations, and workflows
• Work with team members to understand basic business requirements
• Assist in developing simple data models
• Support creation and maintenance of dashboards and reports
• Participate in team meetings, code reviews, and knowledge-sharing sessions
• Collaborate with cross-functional teams including IT, HR, Finance, and Operations
• Learn and apply best practices in analytics engineering, data modeling, and data governance.

Analytics Engineer Intern

• 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

• Help define and standardize key business metrics in collaboration with stakeholders
• Develop and maintain dashboards and reports that give business teams reliable, selfserve access to the metrics that matter most to them
• Build and maintain semantic layer views that serve as the trusted, centralized definitions of key business metrics
• Use AI-assisted development tools to improve the speed and quality of data modeling work and for documentation generation, anomaly detection, and surfacing insights from complex datasets
• Partner closely with analysts and business stakeholders to understand data needs and translate them into durable, reusable solutions
• Serve as a trusted resource for analysts and downstream consumers
• Communicate clearly across technical and non-technical audiences, making complex data concepts accessible without oversimplifying them

• 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

• Maintain and expand the company’s analytical data model using Snowflake and dbt, ensuring datasets are reliable, well-structured, and easy to use.
• Partner with stakeholders to understand reporting and analytics needs and translate them into new models and datasets.
• Investigate discrepancies in metrics and datasets and perform root-cause analysis across systems.
• Monitor and maintain ELT pipelines across our data stack.
• Investigate and resolve pipeline failures, schema changes, and data inconsistencies.
• Identify opportunities to improve pipeline reliability, efficiency, and cost effectiveness.
• Expand documentation across the data model to clearly describe business logic, relationships, and definitions.
• Ensure datasets are clearly structured and documented so they can be reliably used across analytics tools and internal workflows.
• Contribute to the structured AI data context files that help internal AI tools accurately interpret datasets and metrics.
• Help maintain data governance standards, including contributing to PII masking policies and ensuring sensitive customer data is handled appropriately across the data platform.
• Work closely with Product, Revenue, and Operations teams to understand their data needs and questions.
• Help stakeholders navigate the data model and identify the most appropriate datasets for their use cases.
• Occasionally build or modify Hex projects to support data exploration or reporting needs.

• Data Modeling: Research and work with business stakeholders to develop our data warehouse model.
• Data Transformation: Clean, transform, and enrich data to create high-quality datasets suitable for analysis and machine learning.
• Collaboration: Work closely with product teams, software developers, data scientists, and analysts to understand data needs and deliver innovative solutions.
• Data Management: Ensure data accuracy, consistency, and reliability across all datasets.
• Optimization: Optimize data processes for performance and scalability.
• Documentation: Maintain comprehensive documentation of transformation logic and lineage.

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