Tiger Analytics, LLC
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 multiple Fortune 500 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 global analytics 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.
Key Responsibilities
Data Integration – Implement and maintain data synchronization between on‑premises Oracle databases and Snowflake using Kafka and CDC tools. Data Modeling – Assist in developing and optimizing the data model for Snowflake, ensuring it supports analytics and reporting requirements. Data Pipeline Development – Design, build, and manage ETL pipelines using Airflow orchestration and Python scripting to transform raw data into Snowflake. Reporting Support – Collaborate with data architect to structure data in Snowflake for efficient reporting. Technical Documentation – Create and maintain comprehensive documentation of data pipelines, ETL processes, and data models. Tools and Skillsets
Data engineering: proven track record of developing and maintaining data pipelines and integration projects. Databases: Strong experience with Oracle, Snowflake, and Databricks. Data Integration Tools: Proficiency with Kafka and CDC tools for ingestion and synchronization. Orchestration Tools: Expertise with Airflow to manage data pipeline workflows. Programming: Advanced proficiency in Python and SQL for data processing tasks. Data Modeling: Understanding of data modeling principles and experience with data warehousing solutions. Cloud Platforms: Knowledge of cloud infrastructure and services, preferably Azure, as it relates to Snowflake and Databricks integration. Collaboration Tools: Experience with version control systems (e.g., Git) and collaboration platforms. CI/CD Implementation: Utilize CI/CD tools to automate deployment of data pipelines and infrastructure changes. Communication: Excellent communication and teamwork skills, with a detail‑oriented mindset. Strong analytical skills, with the ability to work independently and solve complex problems. 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 Python, SQL, and PySpark Creating and optimizing complex data processing and data transformation pipelines using Python Experience with “Snowflake Cloud Data warehouse” and DBT tool 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 Build processes supporting data transformation, data structures, metadata, dependency and workload management This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility. Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.
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Data Integration – Implement and maintain data synchronization between on‑premises Oracle databases and Snowflake using Kafka and CDC tools. Data Modeling – Assist in developing and optimizing the data model for Snowflake, ensuring it supports analytics and reporting requirements. Data Pipeline Development – Design, build, and manage ETL pipelines using Airflow orchestration and Python scripting to transform raw data into Snowflake. Reporting Support – Collaborate with data architect to structure data in Snowflake for efficient reporting. Technical Documentation – Create and maintain comprehensive documentation of data pipelines, ETL processes, and data models. Tools and Skillsets
Data engineering: proven track record of developing and maintaining data pipelines and integration projects. Databases: Strong experience with Oracle, Snowflake, and Databricks. Data Integration Tools: Proficiency with Kafka and CDC tools for ingestion and synchronization. Orchestration Tools: Expertise with Airflow to manage data pipeline workflows. Programming: Advanced proficiency in Python and SQL for data processing tasks. Data Modeling: Understanding of data modeling principles and experience with data warehousing solutions. Cloud Platforms: Knowledge of cloud infrastructure and services, preferably Azure, as it relates to Snowflake and Databricks integration. Collaboration Tools: Experience with version control systems (e.g., Git) and collaboration platforms. CI/CD Implementation: Utilize CI/CD tools to automate deployment of data pipelines and infrastructure changes. Communication: Excellent communication and teamwork skills, with a detail‑oriented mindset. Strong analytical skills, with the ability to work independently and solve complex problems. 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 Python, SQL, and PySpark Creating and optimizing complex data processing and data transformation pipelines using Python Experience with “Snowflake Cloud Data warehouse” and DBT tool 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 Build processes supporting data transformation, data structures, metadata, dependency and workload management This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility. Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.
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