Join SADA as a Data Engineer!
Your Mission
As a Data Engineer at SADA, you will 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 cover a diverse array of domain areas, such as data migrations, data archival and disaster recovery, and big data analytics solutions requiring a combination of batch or streaming data pipelines, data lakes and data warehouses.
You will be recognized as an established contributor by your team. You will contribute design and implementation components for multiple projects. You will work mostly independently with limited oversight. You will also participate in client-facing discussions in areas of expertise.
Responsibilities:
- Solve complex problems building advanced software systems, while processing several petabytes of data
- Adapt quickly to utilize software engineering best practices
- Demonstrate the ability to deliver quality software collaboratively
- Designing, implementing, and running big data pipelines that canvas over petabytes of data
- Collaborate with your team of engineers, project managers and solutions architecture to build new products and features for our clients.
- Learn functional programming
- Ensure that the software you create is testable and tested
- Actively participate in the architecture, design, and implementation discussions
- Actively participate in planning, execution, and success of complex technical projects
Pathway to Success
#ThinkOneStepAhead: At SADA Systems 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 leads you through the engineering or management growth tracks.
Expectations
Required Travel - Up to 30% travel to customer sites, conferences, and other related events.
Customer Facing - You will interact with customers on a regular basis, sometimes daily, other times weekly/bi-weekly. Common touchpoints occur when qualifying potential opportunities, at project kickoff, throughout the engagement as progress is communicated, and at project close. You can expect to interact with a range of customer stakeholders, including engineers, technical project managers, and executives.
Training - Ongoing with a first-week orientation at HQ followed by a 90-day onboarding schedule. Details of the timeline can be shared.
Job Requirements
Required Credentials:
- Google Professional Data Engineer Certified or able to complete within the first 45 days of employment
Required Qualifications:
- Expertise in at least one of 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 warehouse modernization: building complete data warehouse solutions, including technical architectures, star/snowflake schema designs, infrastructure components, ETL/ELT pipelines and reporting/analytic tools. Must have hands-on experience working with batch or streaming data processing software (such as Beam, Airflow, Hadoop, Spark, Hive).
- Data migration: migrating data stores to reliable and scalable cloud-based stores, including strategies for minimizing downtime. 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. Up to petabytes in scale.
- Experience writing software in one or more languages such as Python, Java, Scala, or Go
- Experience building production-grade data solutions (relational and NoSQL)
- Experience with systems monitoring/alerting, capacity planning and performance tuning
- Experience in technical consulting or other customer facing role
Useful Qualifications:
- Experience working with Google Cloud data products (CloudSQL, Spanner, Cloud Storage, Pub/Sub, Dataflow, Dataproc, Bigtable, BigQuery, Dataprep, Composer, etc)
- Experience with IoT architectures and building real-time data streaming pipelines
- Applied experience operationalizing machine learning models on large datasets
- Knowledge and understanding of industry trends and new technologies and ability to apply trends to architectural needs
- Demonstrated leadership and self-direction -- willingness to teach others and learn new techniques
- Demonstrated skills in selecting the right statistical tools given a data analysis problem