• Own the DataOps lifecycle for our Snowflake-on-AWS platform • Turn data products into reliable services with SLAs/SLOs • Automate promotion across environments • Engineer idempotent pipelines using Streams/Tasks, Snowpipe/Kafka, and orchestration (Airflow/Dagster/Step Functions/Lambda) • Implement the data test pyramid: column/row checks, anomaly detection, reconciliation, and end-to-end validation. • Automate PII classification and object TAGS; enforce tag-based masking, row access policies, RBAC role families, and network policies. • Lead data incident triage, customer comms, RCAs, and post-incident hardening. • Track queries, warehouse utilization, and job cost; implement guardrails.

• Collaborate with stakeholders across the organization to design and implement scalable, cloud-based data solutions, integrating generative AI to drive innovation. • Work closely with cross-functional stakeholders (finance, product, marketing, customer support, tech, data science) to enable trusted data products for internal decision making and external-facing tools. • Take a leading role in the development of a data lake resource to complement our existing data warehouse. • Work with AWS services, automation tools, machine learning, and generative AI to enhance efficiency, stability, security, and performance. • Operate and evolve our Postgres data warehouse: schema design, performance tuning, indexing, access controls, etc. • Build analytics-ready datasets supporting sustainability measurement, supply-chain insights, and business metrics. • Deploy and maintain multiple instances of Cube.dev semantic layers with standardized configuration, CI/CD workflows, and governance practices. • Support integration and deployment of genAI-enabled workflows, especially NLP-based use cases (classification, extraction, normalization, embeddings/similarity). • In collaboration with data scientists, research and develop practical transition plans for evolving selected relational/warehouse data structures into a graph-based knowledgebase.

• Design and implement lakehouse architecture using open-source technologies • Build and optimize ClickHouse deployments for high-performance analytical workloads • Develop custom data transforms and ETL/ELT pipelines using well-supported open-source tools • Create data models that bridge our Postgres application databases with ClickHouse analytics layer • Partner with product and engineering to define data models that serve both analytical and operational needs • Write specifications before writing code—defining contracts, schemas, and expected behaviors upfront • Use AI-assisted coding tools daily to accelerate development and reduce toil • Establish data quality frameworks and observability across the pipeline • Optimize for performance, cost, and reliability at scale

• Design and implement a new and well-architected data platform, utilizing market-leading technologies and Platforms (DataBricks, Snowflake, etc.) • Help accelerate our ongoing migration from legacy data systems (MS SQL, SSIS, SSAS, SSRS) to the new platform • Ingest and aggregate data from both internal and external data sources to build our datasets • Help with data-related engineering topics to enable reporting and dashboarding • Improve the productivity of data analysts and enable a higher degree of self-service • Build data pipelines and data-powered products • Work closely with cross-functional tech teams and drive excellence in our engineering, planning, and architecture • Inspire, guide, and teach professionals in the data team and beyond, about valuable trends and best practices in data engineering • Participate in machine learning, data science, and AI initiatives together with other professional team members • Support our amazing culture where we care about our customers and a productive and healthy team atmosphere.