Platform Data Engineer

 Location: San Francisco, Seattle, LA or PST
  Enterprise Data Platform to enable timely, effective and safe sharing of data to multiple engineering, operations and business teams for building world class data products
 
Responsibilities
Build data ingestion and processing pipelines to enable data analytics and data science use-cases in areas of digital commerce, service operations, charging, reliability, finance, capex, warranty, customer service and others.
Build modular set of data services using Python, SQL, AWS Glue, lambdas, API Gateway, Kafka, data build tool (dbt), Apache Spark on EMR among others
Build automated unit and integration testing pipelines using frameworks like PySpark
Create and manage CICD pipelines with Gitlab CI and AWS Code Pipeline/CodeDeploy
Automate and schedule jobs using Managed Airflow
Build the ODS and reporting schemas and load the data into AWS Redshift or Snowflake
Design and build data quality management services with Apache Deequ and data observability tools like Splunk, DataDog , CloudWatch
Provide a variety of query services with REST, Athena/Presto, server sent events
Configure and setup the enterprise data lineage and meta data management and data catalog support using tools like Collibra/Alation
Assist the data scientist within the data engineering team as well as other software engineering teams with data cleansing, wrangling and feature engineering
Ensure green builds for deployment and work with program management and senior leads to burn down planned deliverables in a sprint cycle
 
Qualifications
At least 5+ years building data and analytics platforms using AWS Cloud, Python and SQL
Knowledge of AWS technologies specifically MSK, EMR, Athena, Glue, lambdas, API Gateway as well as Python, SQL is a must
Knowledge of modern data tools like dbt (data build tool) and Airflow orchestration is highly desired
Ability to assist SQL analysts and Tableau developers in business teams in creating the right set of materialized views in a SQL data warehouse like Redshift/Snowflake
Knowledge of automation and CICD best practices
Familiarity with machine learning and data science ecosystems especially AWS Sagemaker and Databricks is highly preferred
Hands-on experience in building and maintaining production data applications, current experience in both relational and distributed columnar date stores.
Deep experience using SQL, Python, and SparkHands-on experience with Big-data technologies (e.g
Redshift, Athena, Glue, EMR, Kinesis, Step Function, or equivalent in other web services)
Familiarity with timeseries database, data streaming applications, Kafka, Flink, and more is a plus Familiarity with modern data science and product analytics tools and techniques such as R, Machine Learning, and advanced statistics is a plus.