TOCUMULUS
Website:
tocumulus.com
Company:
https://www.linkedin.com/company/tocumulus-services
Industries: IT Services and IT Consulting
Job details:
The Enterprise Data & Data Capabilities team in Technology is focused on a transformative journey that supports our data strategy, including how we develop and deliver critical business outcomes and how we engage with partners across for Data, AI, and BI services.
Our team is seeking an experienced and delivery-focused professional to join as Data Engineer, AI-Assisted Data Pipeline Development, responsible for designing, developing, and modernizing enterprise data pipelines. The role requires hands-on expertise in Snowflake, Python, SQL, and modern data engineering practices, with strong exposure to AI-assisted development techniques that improve pipeline productivity, data quality, automation, and delivery outcomes.
The role involves close collaboration with data services teams, technology partners, product owners, business stakeholders, architects, and senior leadership. The successful candidate will lead and contribute to data pipeline design, ingestion strategy, transformation logic, testing, monitoring, documentation, governance alignment, and delivery execution across enterprise data platforms, AI-enabled solutions, and agile delivery practices.
Responsibilities:
· Lead the analysis, validation, cleansing, enrichment, and preparation of data from multiple sources for reporting, analytics, operational, and AI-enabled use cases.
· Design, develop, test, optimize, and monitor AI-assisted data ingestion pipelines that deliver structured and unstructured data into enterprise platforms, with a strong emphasis on Snowflake-based environments.
· Apply AI-assisted pipeline development approaches to accelerate Python-based data preparation, transformation logic, orchestration support, code generation, troubleshooting, and creation of model-ready datasets for generative AI and analytics workflows.
· Define and maintain technical documentation covering data mappings, transformation rules, lineage, quality controls, operational procedures, runbooks, and production support processes.
· Develop advanced SQL queries, data profiling routines, reconciliation controls, and exception handling processes to identify data issues, anomalies, performance gaps, and improvement opportunities.
· Partner with reporting and analytics teams to design and maintain dashboards, reports, and data products that provide timely, trusted insights to business and technology stakeholders.
· Own or contribute to testing strategies for ETL/ELT pipelines, including test planning, automation opportunities, defect management, performance validation, and release readiness documentation.
· Embed data governance, metadata management, data quality, privacy, security, and auditability standards into pipeline design and day-to-day delivery.
· Collaborate with cross-functional teams to interpret business requirements, shape technical solutions, estimate delivery effort, manage dependencies, and translate requirements into scalable pipeline and data product deliverables.
· Monitor production data jobs, issue triage, analyze logs and data exceptions, coordinate remediation, and support continuous improvement of operational stability and reliability.
· Maintain high-quality delivery documentation in Jira, Confluence, and Microsoft Office to support auditability, transparency, knowledge sharing, and stakeholder communication.
· Provide technical guidance to junior team members and stay current with emerging AI, Snowflake, data engineering, and analytics technologies to drive practical improvements in delivery practices.
Qualifications:
· 8-10 years of overall experience with 5+ most recent years of experience in data engineering, data warehousing, business intelligence, analytics engineering, technology delivery, or related enterprise data roles.
Required skills:
o Strong hands-on knowledge of SQL, data analysis, relational database concepts, dimensional modeling, and enterprise data warehousing patterns.
o Advanced programming or scripting experience using Python, with practical exposure to PySpark, Shell Scripting, APIs, reusable utilities, automation.
o Proven experience designing and implementing ETL/ELT pipelines, AI-assisted pipeline development practices, ingestion frameworks, transformation logic, data quality controls, and production-ready data workflows.
o Hands-on experience with AI technologies, including machine learning, generative AI, prompt-based tools, vector databases, model-ready data preparation, and retrieval-augmented generation concepts.
o Hands-on experience with Snowflake and AWS cloud technology stack; experience building, optimizing, and supporting Python-based data pipelines in cloud data platforms using AI-assisted development tools.
o Strong understanding of data quality, lineage, privacy, controls, auditability, and secure data handling in regulated enterprise environments.
o Strong analytical thinking, problem-solving capability, attention to detail, and ability to operate independently across multiple priorities.
o Experience with workflow orchestration, batch job monitoring, scheduling, and production support tools such as Autosys, Airflow, or similar platforms.
Preferred:
o Familiarity with AI governance practices and controls.
o Relevant certifications in Snowflake, cloud platforms, AI & ML would be a plus.
Education:
Bachelor’s degree in computer science, information systems, data analytics, engineering, mathematics, statistics, business, finance, or a related field required; master’s degree or relevant advanced certifications in data engineering, cloud platforms, Snowflake, AI, or analytics preferred.
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