Data Engineer - Snowflake / AWS / ML Ops
Data Engineer - Snowflake / AWS / ML Ops
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Job Description
Do you love a career where you Experience Grow & Contribute at the same time while earning at least 10% above the market If so we are excited to have bumped onto you.
We are an IT Solutions Integrator/Consulting Firm helping our clients hire the right professional for an exciting long term project. Here are a few details.
Requirements
Position Title: Data Engineer
About the Role:
We are seeking a highly skilled Senior Data Engineer to design build and optimize scalable data architectures that power analytics machine learning and enterprise applications. You will work closely with cross-functional teams applying your expertise in SQL Snowflake Python and cloud-native tools to deliver high-quality secure and efficient data solutions in a fast-paced financial services environment.
Key Expertise Required (Top Priorities):
Deep knowledge of data modeling and data architecture for enterprise-scale applications.
Strong understanding of ML Ops and how it leverages data to deliver predictive and AI-driven outcomes.
Proven experience implementing data architectures that support data science models including prediction machine learning and advanced analytics.
Core Responsibilities:
Data Architecture & Modeling: Design implement and maintain enterprise-grade data architectures ensuring optimal performance scalability and reliability.
ELT Development: Build robust ELT frameworks using DBT MWAA and Snowflake from the ground up.
SQL & Snowflake Expertise: Write optimized SQL queries perform performance tuning and implement best practices in data modeling.
Python Development: Develop automation scripts data integration pipelines and REST API connectors using Python.
Workflow Orchestration: Leverage Apache Airflow for scheduling orchestration and monitoring of data workflows.
Cloud Integration: Work with AWS services (S3 Lambda IAM CloudWatch) to manage data pipelines security and monitoring.
Data Quality & Governance: Implement processes to ensure accuracy consistency and compliance with enterprise data governance and security policies.
ML Ops Integration: Architect data flows that enable machine learning models prediction systems and RAG (Retrieval-Augmented Generation) workflows.
Collaboration & Agile Practices: Partner with UX teams participate in design discussions code reviews and Agile ceremonies to continuously improve deliverables.
Security & Compliance: Follow best practices for secure coding access control and compliance especially for financial data systems.
Required Skills & Experience:
5 years of professional experience as a Data Engineer.
Strong SQL and Snowflake skills including performance tuning and advanced data modeling.
Proven track record in building (not just consuming) ELT frameworks using DBT and MWAA.
Proficiency in Python for automation scripting and REST API integrations.
Hands-on experience with Apache Airflow for orchestration and workflow monitoring.
Expertise with AWS services including S3 Lambda IAM CloudWatch.
Experience in integrating and processing data from REST APIs.
Understanding of data quality governance and cloud-native troubleshooting.
Exposure to RAG workflows and data-driven ML model enablement.
Familiarity with Agile or Scrum methodologies.
Preferred Qualifications:
Experience in financial services or other highly regulated industries.
Strong understanding of secure coding practices and compliance frameworks.
Hands-on ML Ops pipeline development and deployment experience.
Benefits
5+ years of professional experience as a Data Engineer. Strong SQL and Snowflake skills, including performance tuning and advanced data modeling. Proven track record in building (not just consuming) ELT frameworks using DBT and MWAA. Proficiency in Python for automation, scripting, and REST API integrations. Hands-on experience with Apache Airflow for orchestration and workflow monitoring. Expertise with AWS services including S3, Lambda, IAM, CloudWatch. Experience in integrating and processing data from REST APIs. Understanding of data quality, governance, and cloud-native troubleshooting. Exposure to RAG workflows and data-driven ML model enablement. Familiarity with Agile or Scrum methodologies.
Employment Type
Full Time
