Data Engineer

POSITION SUMMARY:

We are seeking a Data Engineer who is passionate about data and eager to make a meaningful impact. In this role, you will design, build, and maintain the core data infrastructure that powers our analytics, machine learning, and data science initiatives. Your responsibilities will include optimizing data management processes, ensuring data quality and reliability, and developing scalable, efficient data models to support advanced analytics and data-driven decision-making. Success in this role requires a strong technical foundation, a collaborative mindset, and a drive to deliver innovative and impactful solutions.

ESSENTIAL FUNCTIONS:

• Data Pipelines:
• Develop and maintain robust ETL/ELT pipelines to ingest data from diverse sources (relational and NoSQL databases, APIs, etc.), including implementing best practices for real-time and batch data ingestion.
• Create and optimize data workflows using modern orchestration tools (e.g., Apache Airflow, Snowflake Tasks, Dagster, Mage).
• Cloud Cost Optimization:
• Monitor and optimize cloud costs (e.g., AWS, Snowflake) by analyzing resource usage and implementing cost-saving strategies.  
• Perform query optimization in Snowflake to reduce compute costs and improve performance.
• Data Foundations:
• Develop and maintain modern data architectures, including data lakes and data warehouses (e.g., Snowflake, Databricks, Redshift), considering trade-offs of different data storage solutions and ensuring alignment with business requirements and SLAs.
• Data Modeling & Transformation:
• Apply dimensional modeling techniques (Kimball), star and snowflake schemas, and normalization vs. denormalization strategies based on use cases.
• Develop transformations using DBT (Core or Cloud), Spark (PySpark), or other frameworks.
• Collaborate on emerging approaches such as data mesh or specialized templates (e.g., Jina) to handle complex data needs.
• Coding:
• Write reusable, efficient, and scalable code in Python, PySpark, and SQL.
• Integrate serverless computing frameworks or modern API frameworks to support data-driven applications (FastAPI, Flask).
• Develop and maintain data-intensive UIs and dashboards using tools like Streamlit, Dash, Plotly, or React.
• Data Quality Control:
• Establish data governance and data quality frameworks, using either custom solutions or popular open-source/commercial tools (e.g., DBT tests, Great Expectations, Soda).  
• Implement data observability solutions to monitor and alert on data integrity and reliability (e.g., Monte Carlo, Alation, or Elementary).
• Define SLAs, SLOs, and processes to identify, troubleshoot, and resolve data issues.
• Teamwork:
• Work cross-functionally with data scientists, analysts, and business stakeholders to translate requirements into robust data solutions.
• Follow and advocate for best practices in version control, CI/CD.
• Document data flows, processes, and architecture to facilitate knowledge sharing and maintainability.

REQUIREMENTS:

EDUCATION & EXPERIENCE: Bachelor's degree in a technical field and 5+ years of industry experience or Master's degree in a technical field and 3+ years of industry experience. (3-5+ years of experience)

TECHNICAL SKILLS (ESSENTIAL):

• Extensive experience with Snowflake (preferred) or other cloud-based data warehousing solutions like Redshift or BigQuery.
• Expertise in building and maintaining ETL/ELT pipelines using tools like Airflow, DBT, Fivetran, or similar frameworks.
• Proficiency in Python (e.g., Pandas, PySpark) for data processing and transformation.
• Advanced SQL skills for querying and managing relational and NoSQL databases (e.g., DynamoDB, MongoDB).
• Solid understanding of data modeling techniques, including dimensional modeling (e.g., star schema, snowflake schema).
• Knowledge of query optimization and cost management strategies for platforms like Snowflake and cloud environments.
• Experience with data quality and observability frameworks (e.g., Great Expectations, Soda).
• Proven expertise in designing and deploying data solutions in the cloud, with a focus on AWS services (e.g., EC2, S3, RDS, Lambda, IAM).
• Experience in building and consuming data-intensive APIs using frameworks like FastAPI or Flask.
• Familiarity with version control systems (e.g., Git) and implementing CI/CD pipelines.

SOFT SKILLS

• Strong communication and collaboration skills with the ability to explain technical concepts to both technical and non-technical audiences.
• Ability to manage multiple priorities and work independently

BONUS POINTS:

• Experience with data streaming platforms such as Kafka or Kinesis.

• Familiarity with Agile methodologies (Scrum, Kanban) and IaC tools like Terraform or CloudFormation.

• Knowledge of emerging technologies or frameworks in the data engineering ecosystem, such as Delta Lake, Iceberg, or Hudi.

Hiring In:

• United States:  AZ, FL, GA, NH, IL, KY, MA, MI, NC, NM, TN, VA , CA
• Canada: Province of Ontario