Fluence
Staff Data Engineer
Fluence, Houston, Texas, United States, 77246
This position will be supporting the Fluence Mosaic team. Fluence Mosaic team’s software technology uses artificial intelligence, advanced price forecasting, portfolio optimization and market bidding to ensure energy storage and flexible generation assets are responding optimally to price signals sent by the market.
You will be an integral member of the Fluence Mosaic team, maturing, developing, and maintaining a data ingestion and storage platform which serves our trading, forecasting, reporting, and analytical applications.
The position will be based in Fluence’s Houston office, with close interaction with a global team of data scientists, product managers, and subject matter experts to shape the evolution of our company and the future of the electricity grid.
What does a Staff Data Engineer do at Fluence?Build, test, scale, and refine data ingestion pipelines across our energy market data platform.Design, develop, and maintain systems for querying and processing data, working with data scientists and software engineers to drive efficient solutions.Build and own data frameworks and libraries, supporting others in deploying, operating, and extending upon your clean, tested code.Help define our data story and enable data-driven solutions at Fluence Mosaic, both technically and culturally.Work with on-call staff, data scientists, software engineers and customer teams to trouble-shoot and debug data pipelines.Help define data management strategy.
What does the ideal candidate look like?10+ years of professional software engineering experience with at least 5+ years of data engineering experience.Excellent written and verbal communication skills.Expert in Python, with hands-on experience with data engineering tools such as Pandas.Knowledgeable in at least one other programming language (C/C++, Kotlin, Java) with the ability to adapt to new environments.Expert in SQL with a strong understanding of database design, data modeling, and query tuning with hands-on experience ingesting, storing and serving time-series data preferred.Hands-on knowledge of PostGres.Experience building Restful services.Understanding of automated testing.Experience with containerization technologies such as Docker.Experience on setting up deployment processes and tools.Knowledge of available data streaming platforms, distributed compute frameworks, and file storage formats with the expertise to apply their use to meet our organizational needs.Experience building data pipelines with stringent latency and correctness requirements.Hands-on experience working with the AWS cloud and AWS services such as S3, Aurora, RDS, EC2, Lambdas, etc.Strong computer science fundamentals, including knowledge of data structures and algorithms.Proven ability to meet deadlines and deliver solutions quickly at high quality.Passionate about learning, and tackling new and exciting technical challenges.Nice to haves:Working knowledge with Terraform.Experience with BI tools such as Looker, PowerBI.Experience with visualization tools in the Python ecosystem such as matplotlib, plot.ly, etc.Experience working with Data Science teams.Experience working with data warehouses such as Snowflake.Experience with Cassandra or Scylla.Experience using Kubernetes.Energy industry experiences.
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You will be an integral member of the Fluence Mosaic team, maturing, developing, and maintaining a data ingestion and storage platform which serves our trading, forecasting, reporting, and analytical applications.
The position will be based in Fluence’s Houston office, with close interaction with a global team of data scientists, product managers, and subject matter experts to shape the evolution of our company and the future of the electricity grid.
What does a Staff Data Engineer do at Fluence?Build, test, scale, and refine data ingestion pipelines across our energy market data platform.Design, develop, and maintain systems for querying and processing data, working with data scientists and software engineers to drive efficient solutions.Build and own data frameworks and libraries, supporting others in deploying, operating, and extending upon your clean, tested code.Help define our data story and enable data-driven solutions at Fluence Mosaic, both technically and culturally.Work with on-call staff, data scientists, software engineers and customer teams to trouble-shoot and debug data pipelines.Help define data management strategy.
What does the ideal candidate look like?10+ years of professional software engineering experience with at least 5+ years of data engineering experience.Excellent written and verbal communication skills.Expert in Python, with hands-on experience with data engineering tools such as Pandas.Knowledgeable in at least one other programming language (C/C++, Kotlin, Java) with the ability to adapt to new environments.Expert in SQL with a strong understanding of database design, data modeling, and query tuning with hands-on experience ingesting, storing and serving time-series data preferred.Hands-on knowledge of PostGres.Experience building Restful services.Understanding of automated testing.Experience with containerization technologies such as Docker.Experience on setting up deployment processes and tools.Knowledge of available data streaming platforms, distributed compute frameworks, and file storage formats with the expertise to apply their use to meet our organizational needs.Experience building data pipelines with stringent latency and correctness requirements.Hands-on experience working with the AWS cloud and AWS services such as S3, Aurora, RDS, EC2, Lambdas, etc.Strong computer science fundamentals, including knowledge of data structures and algorithms.Proven ability to meet deadlines and deliver solutions quickly at high quality.Passionate about learning, and tackling new and exciting technical challenges.Nice to haves:Working knowledge with Terraform.Experience with BI tools such as Looker, PowerBI.Experience with visualization tools in the Python ecosystem such as matplotlib, plot.ly, etc.Experience working with Data Science teams.Experience working with data warehouses such as Snowflake.Experience with Cassandra or Scylla.Experience using Kubernetes.Energy industry experiences.
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