12+ years of industry experience in data engineering with a focus on AWS Cloud and AI.
8+ years of experience in building and deploying data processing pipelines in production.
Advanced proficiency in Python, SQL, and PySpark.
Experience with data processing and transformation pipelines using Python.
Deep experience with Snowflake/Databricks on AWS and distributed computing frameworks.
Understanding of Data Warehouse systems and migration to data lakes.
Strong analytical skills with unstructured datasets.
Responsibilities
Architect and design advanced analytics capabilities.
Implement large-scale data processing pipelines.
Create and optimize complex data transformation processes.
Support and manage data transformation, structures, and metadata.
Collaborate with cross-functional teams to solve business challenges.
Utilize visualization tools to generate insights from data.
Benefits
Significant career development opportunities.
Opportunity to work in a small, entrepreneurial environment.
High degree of individual responsibility in projects.
Full Job Description
The Data Engineer will be responsible for architecting, designing, and implementing advanced analytics capabilities. The right candidate will have broad skills in database design, be comfortable dealing with large and complex data sets, have experience building self-service dashboards, be comfortable using visualization tools, and be able to apply your skills to generate insights that help solve business challenges. We are looking for someone who can bring their vision to the table and implement positive change in taking the company's data analytics to the next level.
Requirements
12+ years of overall industry experience specifically in data engineering with a heavy focus on the AWS Cloud stack and AI.
8+ years of experience building and deploying large-scale data processing pipelines in a production environment.
Advanced proficiency in Python, SQL, and PySpark.
Creating and optimizing complex data processing and data transformation pipelines using python
Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases
Deep experience with Snowflake/Databricks on AWS, dbt, and distributed computing frameworks like Apache Spark.
Understanding of Datawarehouse (DWH) systems, and migration from DWH to data lakes/Snowflake
Understanding of ELT and ETL patterns and when to use each. Understanding of data models and transforming data into the models
Strong analytic skills related to working with unstructured datasets
Build processes supporting data transformation, data structures, metadata, dependency and workload management
Experience supporting and working with cross-functional teams in a dynamic environment
Benefits
Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, challenging, and entrepreneurial environment, with a high degree of individual responsibility.
About Tiger Analytics
Tiger Analytics is a consulting firm that provides data analytics consulting services to businesses. The company specializes in data science, machine learning, and artificial intelligence. Tiger Analytics helps businesses to leverage data to make better decisions, improve operations, and drive growth. The company has worked with clients in a variety of industries, including healthcare, retail, finance, and technology.
