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Data Engineering & Solution Delivery
Design, develop, test, deploy, and support configuration-driven ELT/ETL pipelines that acquire data from enterprise applications, databases, APIs, file repositories, partner data sources, and shared data exchanges.
Build reusable ingestion and transformation patterns for structured, semi-structured and unstructured data, including relational data, JSON, XML, CSV, Excel, and document metadata.
Develop curated data products and dimensional models using star and snowflake techniques to support enterprise reporting, semantic models, analytics, and downstream operational use cases.
Implement transformations using SQL, Python, Spark/PySpark, Databricks notebooks, dbt Cloud, Snowpark, and related cloud-native engineering tools.
Contribute hands-on to modernization initiatives, including migration from legacy data warehouse and ETL patterns to lakehouse, cloud warehouse, and ELT-based architectures.
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Platform Reliability, Security & Operations
Monitor and optimize pipelines, storage, compute, and databases for performance, scalability, availability, and cost efficiency.
Implement data quality controls, reconciliation, exception handling, observability, logging, alerting, and service-level measures for critical data flows.
Lead or support incident triage, root-cause analysis, restoration, and follow-through on preventive actions for production data services.
Apply secure engineering practices including managed identity, role-based access, encryption, secrets management, least-privilege access, and sensitive-data handling requirements.
Implement and maintain CI/CD, automated testing, version control, release management, and environment promotion practices for data workloads.
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AI, Search & Advanced Analytics Enablement
Engineer governed, high-quality data and document pipelines that support enterprise search, AI-assisted knowledge discovery, retrieval-augmented generation, analytics, and data science use cases.
Prepare and curate source data, metadata, permissions, lineage, and quality signals needed for reliable AI solutions while preserving source-system security and access policies.
Partner with Knowledge Management, AI, Business Intelligence, and Information Security teams to define fit-for-purpose data products for model grounding, evaluation, and responsible use.
Help establish repeatable patterns for document ingestion, chunking-ready content preparation, semantic metadata, and controlled data exchange across internal teams and affiliates.
Evaluate emerging data and AI platform capabilities through proofs of concept and recommend adoption based on value, security, sustainability, and architectural fit.
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Engineering Excellence & Collaboration
Conduct design and code reviews, contribute to engineering standards, and promote reusable patterns for data modeling, orchestration, testing, observability, and documentation.
Collaborate with architects and product teams to clarify requirements, assess trade-offs, estimate delivery, and convert solution designs into actionable implementation plans.
Mentor data engineers and contingent staff through pairing, technical guidance, review, and knowledge sharing; may coordinate work across small delivery efforts.
Create and maintain technical documentation, runbooks, data mappings, lineage, and operational procedures; contribute metadata and descriptions for synchronization with Collibra.
Work effectively in Agile delivery teams using Scrum, Jira, and DevSecOps practices while communicating clearly with technical and non-technical partners.