Data Engineer

Snowflake Data Engineer Location: Richardson, TX (onsite) Contract We are looking for a Senior Data Platform Engineer candidate who will design and operate data pipelines that connect heterogeneous source systems, normalize data into consumption‑ready models in Snowflake, and build AI‑powered agents that surface intelligence across the platform. Core Responsibilities Snowflake engineering: design schemas, write performant SQL, manage roles, warehouse sizing, and implement change management practices. ETL/ELT development: build and maintain pipelines that ingest from diverse sources (APIs, databases, event streams) and normalize data for BI and downstream consumers. AI agent development: leverage Snowflake Cortex and AI agent frameworks to build intelligent data products and automate analytical workflows. Backend API connectivity: develop backend integrations with internal and third‑party systems via REST APIs and backend services. Required Skills Snowflake hands‑on experience building and optimizing data models, writing advanced SQL (PIVOT, GROUPING SETS, ROLLUP/CUBE), and managing Snowflake environments in production. Multisource integration: proven ability to connect and ingest data from heterogeneous sources including relational databases, REST APIs, SaaS platforms, and event streams. ETL/ELT design: experience designing normalized schemas and transformation pipelines that produce clean, consumption‑ready data models using snowflake‑schema dimensional modeling. Python: strong proficiency for data engineering tasks, pipeline orchestration, data transformation, API clients, and scripting automation. AI agents within Snowflake: familiarity with Snowflake Cortex LLM functions and agent‑based patterns for building intelligent, data‑driven workflows inside the Snowflake ecosystem. Backend integration patterns: practical experience building backend services and integrations using Python, REST APIs, and related tooling (authentication, pagination, error handling, retry logic). Additional Skills PostgreSQL relational databases: working knowledge of PostgreSQL or equivalent RDBMS, including query optimization, indexing, and schema design patterns. Go (Golang): experience building backend services or microservices in Go is a strong differentiator. Cloud data infrastructure: familiarity with Azure or AWS data services (e.g., Azure Data Factory, Event Hubs, S3 as source or orchestration layers). Data observability and testing: experience with data quality frameworks (dbt tests) or observability tooling (Great Expectations, Monte Carlo, etc.). You’re a self‑directed engineer who thrives at the intersection of data engineering and platform thinking, can navigate ambiguous requirements, design for scale, and communicate clearly across engineering and product stakeholders. You care about data quality, documentation, and building systems that other teams love to consume. Skills Mandatory Skills: ETL concepts, Python for data, Snowflake. Good to Have Skills: AWS S3, Azure Data Factory, PostgreSQL, Python – Data Science, Snowflake AI/ML. #J-18808-Ljbffr