Tiger Analytics is a fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best analytics global consulting team in the world.
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.
Responsibilities:
- A technologist and engineer at heart and be comfortable in enabling new technology and being hands on with the execution of the strategy. They must exhibit a deep understanding of modern AWS Data stack and agile delivery models, demonstrated focus on building business data pipelines.
- Functions as senior member of an agile team and helps drive consistent development practices, tools usage, common components, and patterns.
- Direct the identification and recommendation of appropriate solutions.
- Performs hands-on design and development with PySpark/Pandas/SQL, AWS Glue for ETL and Catalog, Datasync, Parquet files, Warehousing experience preferably with Snowflake and/or Redshift
- Spends significant amount of time writing code and testing.
Requirements
- 8+ years of overall industry experience specifically in data engineering
- 5+ years of experience building and deploying large-scale data processing pipelines in a production environment.
- Strong experience in building ETL data pipelines and analysis using Python, SQL, and PySpark
- In-depth knowledge of data engineering concepts, techniques, and best practices.
- Creating and optimizing complex data processing and data transformation pipelines using python
- Knowledge of programming languages in data engineering such as Python or PySpark
- Experience with ETL tools and frameworks (e.g., Apache NiFi, Talend, AWS Glue, Azure Data Factory).
- Knowledge of big data platforms like Snowflake, DBT, AWS Redshift, Postgres, MongoDB, and Hadoop
- Experience with data pipeline and workflow management tools
- Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases
- 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
- Cloud certification (AWS Certified Big Data - Specialty, Microsoft Certified: Azure Data Engineer) is a plus.
- 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.