Data Engineer

We’re looking for a rare kind of engineer — someone who doesn’t wait to be told what to do, figures things out, and brings both technical depth and sharp communication to the table. This isn’t a ticket‑taker role. You’ll own end‑to‑end delivery across modern data platforms and full stack applications, work directly with business stakeholders, and be expected to lead technically without hand‑holding.

If you’re the kind of person who reads documentation on weekends because you’re genuinely curious, explains complex systems clearly to non‑technical leaders, and takes pride in clean, reliable pipelines — keep reading.

• Data Platform Engineering (Snowflake & dbt)
• Design, build, and maintain dbt models and Snowflake stored procedures powering enterprise data pipelines
• Own data transformation logic, CDC processing, curated layer development, and reporting enhancements
• Debug pipeline failures, tune performance, and validate data quality end to end
• Translate business requirements into robust, scalable technical solutions independently
• Build and maintain Streamlit applications used for business reporting and operational monitoring
• Develop backend validation processes and analyze AI‑generated outputs for accuracy and rule compliance
• Debug application failures, trace root causes through logs, and implement lasting fixes
• Manage integrations across Streamlit, Snowflake, and backend data services
• Work on streaming and real‑time data integration pipelines
• Troubleshoot ingestion failures, monitor job health, and validate end to end data flow
• Manage Azure Storage, Event Hub, and secrets/configuration for secure connectivity
• Manage and execute Databricks/PySpark workflows for high‑volume use cases
• Support ad‑hoc processing requests including government reporting pipelines
• Validate outputs and ensure successful pipeline execution under deadline pressure
• Provide end to end support across APIs, cloud platforms, analytics apps, and data pipelines
• Participate actively in enhancement requests, operational maintenance, and issue resolution

• 7–8+ years of hands‑on experience in data engineering
• Strong proficiency in Snowflake, dbt, and SQL‑based data modeling — this is the core of the role
• Proven experience integrating multiple enterprise systems, including SAP and similar source platforms
• Working knowledge of Informatica for data integration and pipeline orchestration
• Working knowledge of DB2 and experience handling legacy database environments
• Hands‑on experience with Apache Kafka and real‑time/streaming data integration patterns
• Experience with Databricks and PySpark for large‑scale data processing
• Proficiency in Python — required, not negotiable
• Experience building Streamlit applications for reporting, monitoring, or operational workflows — required
• Practical experience with AI development tooling — including working with LLMs, prompt engineering, AI/ML frameworks, vector databases, and integrating AI‑generated outputs into data workflows
• Hyperscaler experience with Microsoft Azure as primary; familiarity with AWS or GCP is a plus
• Hands‑on with Azure services — Blob Storage, Event Hub, Key Vault, and related tooling
• Comfortable working across the stack: APIs, pipelines, cloud infrastructure, and front‑end tooling

• Self‑starter. You don’t wait for someone to define the problem — you go find it
• Fast, independent learner. You pick up new technologies, frameworks, and domains quickly and without being told to
• High ownership. You treat production systems like they’re yours and take failure personally (in a healthy way)
• Detail‑oriented. You catch the thing everyone else missed
• Intellectually sharp. You can hold complexity in your head and still produce clean, maintainable work
• Strong verbal and written communication skills — you can explain a data pipeline to an engineer and a business outcome to an executive, adjusting your language accordingly
• Comfortable presenting work, trade‑offs, and recommendations to both technical teams and non‑technical stakeholders
• Collaborative but independent — you work well with others but don’t need to be managed
• Experience working in cross‑functional environments alongside business analysts, product owners, and leadership

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent professional experience)
• Prior experience in automotive, manufacturing, logistics, or similarly data‑intensive industries is a plus
• Exposure to AI/ML output validation or AI‑integrated data workflows
• Experience supporting government or regulatory reporting pipelines