Data Analytics and Engineering Professional

General Function

The Enterprise Data Analytics team is seeking a hands-on data engineer who can independently design, build, deploy, and support reliable data solutions across a complex enterprise technology environment.

The Engineer - Data Analytics and Engineering will develop the pipelines, integrations, data models, cloud services, automation, and AI-enabled solutions that support enterprise reporting and operations.

This position emphasizes SQL, Python, cloud engineering, data integration, production reliability, and technical problem-solving. The Engineer will own assigned solutions from discovery through deployment and support, working with limited supervision and applying sound technical judgment.

Key Responsibilities

Data Engineering and Integration

• Design, develop, test, deploy, and maintain ETL and ELT pipelines across enterprise applications, APIs, databases, cloud platforms, files, and event-driven services.
• Integrate structured, semi-structured, and unstructured data from ERP, CRM, HCM, ITSM, financial, monitoring, customer, and operational systems.
• Build integrations using REST and GraphQL APIs, database connectors, cloud storage, secure file transfer, and message- or event-based interfaces.
• Design solutions that appropriately handle schema changes, duplicate records, late-arriving data, pagination, rate limits, and partial failures.
• Develop reusable libraries, templates, and configuration-driven processes that reduce one-off development.
• Own assigned pipelines and integrations from technical discovery through production support.

Snowflake, SQL, and Data Modeling

• Design and maintain Snowflake databases, schemas, tables, views, secure views, dynamic tables, streams, tasks, stored procedures, and user-defined functions.
• Develop complex SQL and Snowpark solutions for ingestion, transformation, enrichment, reconciliation, and publication.
• Build scalable dimensional models, analytical marts, curated data products, and semantic-ready datasets.
• Optimize query performance, warehouse utilization, storage patterns, refresh processes, and overall platform cost.
• Select the appropriate balance between SQL transformations, Python processing, AWS services, and external compute.
• Support secure data sharing, downstream applications, embedded analytics, and governed enterprise reporting.

• Build data pipelines and services that prepare structured and unstructured enterprise data for AI and machine-learning use cases.
• Prototype and productionize AI-assisted workflows using capabilities such as Snowflake Cortex, Amazon Bedrock, document extraction, classification, summarization, embeddings, vector search, and retrieval-augmented generation.
• Integrate AI capabilities into operational workflows while ensuring that outputs are traceable, testable, secure, and grounded in authorized data.
• Develop evaluation methods for AI solutions, including benchmark datasets, accuracy measures, exception handling, human review, and operational monitoring.
• Apply appropriate controls for sensitive data, model inputs, generated outputs, access, and retention.

Quality, Reliability, and Production Support

• Monitor pipeline execution, data freshness, processing volumes, failures, resource consumption, and service-level expectations.
• Troubleshoot production issues across source systems, APIs, networks, pipelines, Snowflake, cloud services, and downstream applications.
• Perform root cause analysis and implement durable corrective actions rather than recurring manual fixes.
• Develop technical documentation, architecture diagrams, operational runbooks, recovery procedures, and incident summaries.
• Identify technical risks, security concerns, scalability limitations, and support requirements before production deployment.
• Contribute to technical standards, reference architectures, naming conventions, and reusable engineering patterns.
• Support enterprise data governance initiatives and contribute to data cataloging and metadata management.
• Identify opportunities to automate manual reporting, streamline data flows, and enhance visibility across systems.

Qualifications

• Bachelor s degree in Computer Science, Software Engineering, Information Systems, Data Engineering, Mathematics, or a related discipline, or equivalent professional experience.
• Four or more years of experience in data engineering, software engineering, analytics engineering, data integration, or a related field.
• Advanced SQL skills, including complex transformations, procedural logic, performance tuning, and data-model development.
• Strong Python development skills and experience building production scripts, applications, services, or data-processing workflows.
• Hands-on experience designing and supporting ETL or ELT pipelines in an enterprise or cloud environment.
• Experience with Snowflake, AWS, Azure, Databricks, or another modern cloud data platform.
• Professional fluency in written and spoken English, with the ability to independently participate in technical discussions and explain complex concepts clearly.
• Demonstrated ability to design, troubleshoot, and support production solutions with limited day-to-day supervision.
• Strong technical judgment, problem-solving, documentation, and cross-cultural collaboration skills.

Preferred Qualifications

• Advanced experience with Snowflake, including Snowpark, tasks, streams, dynamic tables, stored procedures, performance optimization, and account-usage monitoring.
• Experience with AWS serverless, integration, orchestration, monitoring, and security services.
• Experience with Snowflake Cortex, Amazon Bedrock, generative AI, document processing, embeddings, vector search, or retrieval-augmented generation.
• Experience integrating with enterprise platforms such as Oracle Fusion, ServiceNow, Salesforce, Microsoft Graph, or operational-monitoring systems.
• Experience with event-driven architectures, serverless applications, queues, notifications, and distributed workflow orchestration.
• Working knowledge of Amazon QuickSight or another BI platform sufficient to support downstream analytics requirements.
• Experience in a managed services provider, consulting organization, technology services company, or complex multi-customer environment.