Required for Investment Life Insurance Company Data Technical Lead – ETL

saraaraby
Mar 12, 2022
2 min read

Vacancy Conditions:

Remote

Full-time

$ 120K-135K/hour

Mandatory Requirements

Must be a US Citizen or Green Card Holder
8+ years of experience as a Data Engineer with ETL background
Strong Experience with Snowflake, AWS cloud, MS SQL Server
Strong Experience with Python, Apache Kafka (or Mulesoft)
Experience in handling Entreprise Data (data movement - data prep - data knowledge)
Financial Services or Life Insurance domain experience

Job Description

You would be working in a fast-paced environment as part of the team of data engineers, data analysts, designing and implementing solutions that provide business-critical insights. You will be responsible for the design of data warehouse schemas as well as end-to-end design and implementation of fault-tolerant and scalable data processing pipelines using a variety of technologies for orchestrating data movement. The primary technologies you would be using are Snowflake, MS SQL Server, Apache Kafka (o Mulesoft), and Python.

Design and implement data warehouse schemas to store large data sets that can be easily and efficiently queried and joined together for reporting and analytical purposes.
Design and develop ETL/ELT pipelines to efficiently move and aggregate data, while optimizing for maximum code and data reuse.
Collaborate with other team members on improvements to existing systems
Investigate data anomalies and provide quick resolutions.
Provide technical support to business users and analysts.

This will include policy data, agent data, work-flow data - as applications come in the door - how can we see what's happening with those. This will end up working with salesforce and interaction data - getting call data - website traffic, etc. The tech lead will be working through what comes first.

Role, Responsibilities & Requirements

What are they looking for?

8+ years of working experience with large scale data warehouse systems
Very strong knowledge of SQL and data manipulation best practices
Experience in building efficient and fault-tolerant ELT/ETL data pipelines
Very strong knowledge of working with large scale datasets
Ability to investigate data discrepancies and find the root cause
Ability to write test cases for data validation
Ability to write clear documentation
Very strong knowledge of data modeling and data warehouse design
Knowledge of Snowflake is preferrable
Knowledge of DBT, Jinja scripting and Airflow is a big plus
Ability to translate business requirements into functional specifications
Ability to quickly adapt to changes in requirements and to effectively work on geographically diverse cross-functional teams
Excellent written and verbal communication skills