Data Engineer II

As a Data Engineer II here at Honeywell Aerospace, you will play an essential role in developing and refining data pipelines that facilitate the accurate and efficient flow of information across various systems. Your efforts will be pivotal in enhancing the organization’s ability to access and utilize critical financial data seamlessly.

You will report to our Sr Data Analytics Manager and work out of our Sky Harbor, Phoenix, AZ location. This position will begin with 90 days onsite before transitioning to a hybrid, 3 days in the office, 2 days at home, position.

Your primary focus will be on creating robust, scalable solutions that ensure real-time delivery and automated processing of data from our diverse data sources to endpoint solutions. The role emphasizes collaboration with Finance stakeholders (not enterprise IT architecture) to translate business requirements into reliable, well‑documented data products.

Key Responsibilities:

Build and maintain ELT/ETL pipelines that ingest data from varied sources (financial systems, ERP/CRM, files, APIs) into Snowflake ; ensure reliability, observability, and recoverability.

Develop transformations using SQL and Python within Dataiku (recipes, flows, scenarios) and Databricks (notebooks, Jobs, Delta Lake/Spark) to produce trusted, reusable finance data marts and subject‑area tables.

Automate and orchestrate workflows (scheduling, dependency management, alerts) in Dataiku/Databricks; implement robust logging and monitoring.

Enable Tableau analytics by publishing performant Snowflake views and semantic layers; optimize query patterns (warehouses, micro‑partitions, caching) to support dashboard performance and scalability.

Data quality and governance: implement validation rules, reconciliations/tie‑outs to source systems, lineage documentation, and access controls; uphold standards for PII and financial data.

Requirement gathering & stakeholder collaboration: work directly with Finance/FP&A, Accounting, and business analysts to translate metrics (e.g., period close, variance analysis, forecasting) into data models and pipelines.

Performance tuning: leverage Snowflake features (e.g., Tasks, Streams, Dynamic Tables where appropriate), clustering strategies, and cost‑efficient warehouse configurations.

Version control and CI/CD: manage code in Git, contribute reviews, unit/integration tests, and promote changes through dev/test/prod.

Process modernization: convert manual/Excel processes into automated, auditable pipelines; prototype utilities and frameworks in Python.