Software Engineer

Requirements

• Orchestration: Airflow/Prefect/ADF/Composer.
• IaC: Terraform/Azure Bicep/CloudFormation; policy-as-code (e.g., OPA/Conftest).
• Streaming: Kafka/Kinesis/PubSub; Change Data Capture.
• dbt (models, tests, exposures) and semantic layer concepts.
• Basic SCD/CDC design patterns; Dimensional modeling (Kimball).
• Security: OWASP, least privilege, key rotation, secret stores (AWS Secrets Manager/Azure Key Vault).
• Observability: OpenLineage, DataDog/CloudWatch/Log Analytics; SLA/SLO thinking.
• Exposure to MLOps (feature stores, model serving) is a plus

Benefits

Additional Information

We're looking for a Mid Data Engineer who's eager to build reliable, secure, and scalable data pipelines while actively adopting AI‑assisted engineering practices. You'll work with Python, Snowflake, and cloud‑native services to support analytics, reporting, ML workflows, and data‑driven products. You'll operate with a DevSecOps mindset, treating security, testing, automation, and AI‑enabled productivity as first‑class citizens. This role expects continuous learning and thoughtful use of tools like GitHub Copilot to improve quality, speed, and maintainability-not to replace engineering judgment What You'll Do (Key Responsibilities) Data Pipelines & ETL/ELT Develop, schedule, and monitor ELT/ETL pipelines in Python to ingest data from APIs, files, and databases into Snowflake. Implement transformations (SQL & Python) following modular, reusable patterns. Leverage AI-assisted tooling (e.g., GitHub Copilot) to accelerate development, improve readability, and reduce boilerplate-while ensuring correctness through reviews and testing Snowflake Engineering Build tables, views, stages, and secure data shares. Tune queries and warehouse configs; manage roles, RBAC, and resource usage. Use AI assistance to analyze query performance, refactor SQL, and identify optimization opportunities Cloud & Orchestration Use cloud services (AWS/GCP/Azure) for storage, compute, and event triggers (e.g., S3/Blob/GCS; Lambda/Functions). Orchestrate jobs (Airflow/Cloud Composer/Prefect/Azure Data Factory) with proper alerting & SLAs. Apply AI tools to speed up pipeline scaffolding, DAG generation, and configuration validation DevSecOps & Reliability Apply CI/CD for data code (linting, unit/data tests, IaC review gates). Embed security scanning (dependencies, IaC policies), secrets management, and least‑privilege IAM. Contribute to observability (logging, metrics, lineage, data quality checks). Use AI responsibly to assist with test generation, documentation, and failure analysis Collaboration Work with Analytics/ML teams to productize models and enable self‑service. Write clear documentation (readme, runbooks, data dictionaries). Actively learn and upskill in AI‑driven engineering practices, sharing learnings with the team 3-6 years of experience in data engineering, analytics engineering, or backend engineering (internships/co-ops included). Programming: Python (pandas, SQLAlchemy/DB APIs, typing, testing with pytest). SQL & Warehousing: Strong SQL; experience with Snowflake (warehouses, stages, tasks, Snowpipe, RBAC). Cloud Basics: Familiarity with one major cloud (AWS/Azure/GCP) and storage + compute + IAM fundamentals. DevSecOps Mindset: Git, CI/CD (GitHub Actions/Azure DevOps/GitLab CI), code reviews, secret handling, dependency scanning. Data Quality: Exposure to unit tests, data validation (e.g., Great Expectations/dbt tests), and monitoring principles. AI‑Aware Engineering: Experience or willingness to adopt GitHub Copilot or similar AI tools Ability to critically evaluate AI‑generated code and ensure correctness, security, and performance Strong problem-solving, curiosity, and ability to learn quickly.

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