Senior Staff Data Engineer

Join SADA as a Senior Data Engineer, Corporate!

Your Mission 

As a Senior Data Engineer, Corporate at SADA, you will have the opportunity to work with big data and emerging Google Cloud technologies to drive corporate services. You will have an opportunity to design, develop, and maintain the best Enterprise Data Warehouse solution to fit our corporate needs. You will be interacting with all of our business units and Google Cloud subject matter experts.

From transforming business requirements, solution architecture, data modeling, architecting, ETL, metadata, and business continuity, you will have the opportunity to work collaboratively with architects and other engineers to recommend, prototype, build, and debug data infrastructures on Google Cloud Platform (GCP). You will have an opportunity to work on real-world data problems facing our customers today. Engagements vary from being purely consultative to requiring heavy hands-on work and covering a diverse array of domain areas, such as data migrations, data archival and disaster recovery, and big data analytics solutions requiring batch or streaming data pipelines, data lakes, and data warehouses. 

You will be expected to run point on whole projects, end-to-end, and to mentor less experienced Data Engineers. You will be recognized as an expert within the team and will build a reputation with Google and our customers. You will demonstrate repeated delivery of project architectures and critical components that other engineers demur to you for lack of expertise. You will also participate in early-stage opportunity qualification calls, as well as guide client-facing technical discussions for established projects. 

Pathway to Success 

#BeOneStepAhead: At SADA, we are in the business of change. We are focused on leading-edge technology that is ever-evolving. We embrace change enthusiastically and encourage agility. This means that not only do our engineers know that change is inevitable, but they embrace this change to continuously expand their skills, preparing for future customer needs. 

Your success starts by positively impacting the direction of a fast-growing practice with vision and passion. You will be measured quarterly by the breadth, magnitude, and quality of your contributions, your ability to estimate accurately, customer feedback at the close of projects, how well you collaborate with your peers and the consultative polish you bring to customer interactions.  

As you continue to execute successfully, we will build a customized development plan together that takes you through the engineering or management growth tracks. 

Expectations

Internal Facing - You will interact with internal customers and stakeholders regularly, sometimes daily, other times weekly/bi-weekly. Expectations will be to capture requirements and deliver solutions suitable for corporate divisions.

Onboarding/Training - The first several weeks of onboarding are dedicated to learning and will include learning materials/assignments and compliance training, and meetings with relevant individuals. Details of the timeline are shared closer to the start date. 

Job Requirements

Required Credentials:

• Google Professional Data Engineer Certified or able to complete within the first 45 days of employment 

Required Qualifications: 

• Mastery in the following domain area:
• Data warehouse modernization: building complete data warehouse solutions on BigQuery, including technical architectures, star/snowflake schema designs, query optimization, ETL/ELT pipelines, and reporting/analytic tools. Must have expert-level experience working with Google’s batch or streaming data processing solutions (such as BigQuery, Dataform, and BI Engine)
• Proficiency in the following domain areas:
• Big Data: managing Hadoop clusters (all included services), troubleshooting cluster operation issues, migrating Hadoop workloads, architecting solutions on Hadoop, experience with NoSQL data stores like Cassandra and HBase, building batch/streaming ETL pipelines with frameworks such as Spark, Spark Streaming, and Apache Beam, and working with messaging systems like Pub/Sub, Kafka and RabbitMQ. 
• Data Catalog: Managing Data Catalogs, definitions, and data lineage.
• Data Quality: Must have experience with DataForm, or other DQ solutions.
• Data migration: migrating data stores to reliable and scalable cloud-based stores, including strategies for minimizing downtime. It may involve conversion between relational and NoSQL data stores, or vice versa
• Backup, restore & disaster recovery: building production-grade data backup and restore, and disaster recovery solutions.