Data Engineer

5 Months ago • 3-5 Years

Job Summary

Job Description

The Software Engineer role at Enphase Energy is responsible for supporting the Enphase Analytics and Data-warehouse team, providing support for the data management and governance activities. This role involves collaborating with various teams like Engineering, Quality, CS, and Factory. The engineer will design, develop, and maintain analytics, dashboards, automation, and reports, working closely with stakeholders to understand their needs and translate them into solutions. Key responsibilities include ensuring performance optimization, quality, and responsiveness of dashboards, identifying and correcting bottlenecks, and maintaining code quality.

Must have:

Strong understanding of Datawarehouse Concepts.
Experience translating theoretical concepts into engineered features.
Expertise in crafting custom features.
Advanced knowledge in Apache Spark (PySpark, SparkSQL, SparkR).
Hands-on experience with cloud-based machine learning platforms.
Demonstrated capability to develop and implement custom statistical algorithms.
Proficiency in statistical methods for identifying patterns.
Experience optimising code for performance.
Familiarity with end-to-end analytical architectures.
Experience creating insightful dashboards and reports.
3+ years of experience as a Data Engineer/Data Science.
Strong problem-solving skills.
Strong Expertise in Python, Spark, R, SQL or any other programming language.
Strong hands-on expertise with at least one analytics tools.

12 skills required

12 skills required for this role

Add these skills to join the top 1% applicants for this job

algorithms

ci-cd

kubernetes

spark

aws

python

docker

sql

data-science

tableau

power-bi

data-analytics

Job Details

Enphase Energy is a global leader in solar inverters. By combining the power of solar energy and the proven advantages of communications technology, Enphase Energy makes solar power systems productive, reliable, smart and safe - increasing the energy harvest of solar panels by up to 25 percent. Our microinverter system is profoundly changing the way solar systems function, and thus, changing the solar industry itself.

As we continue our exciting growth, we are building teams with highly talented individual contributors and leaders who design, develop, and manufacture next generation solar technologies. Our work environment is fast-paced, fun, and full of exciting new projects.

The Software Engineer role will be responsible for Supporting Enphase Analytics and Data-warehouse team. Provides support for the data management/Governance activities of the organisation. Collaborates with Engineering/Quality/CS teams/Factory and various other teams.

Overview:

Design, develop, and maintain Analytics/dashboards/Automation and reports.
Work closely with stakeholders to understand their needs and translate them into business/DW solutions.
Ensure the performance optimisation, quality, and responsiveness of the dashboards.
Collaborate with the team to define, design, and ship new features.
Identify and correct bottlenecks and fix bugs.
Help maintain code quality, organisation, and automatisation.

Essential duties/responsibilities:

Strong understanding of Datawarehouse Concepts.
Experience translating theoretical concepts into engineered features, with a demonstrated ability to create features capturing important events or transitions within the data.
Expertise in crafting custom features that highlight unique patterns specific to the dataset or problem, enhancing model predictive power. Ability to combine and synthesise information from multiple data sources to develop more informative features.
Advanced knowledge in Apache Spark (PySpark, SparkSQL, SparkR) and distributed computing, demonstrated through efficient processing and analysis of large-scale datasets. Proficiency in Python, R, and SQL, with a proven track record of writing optimized and efficient Spark code for data processing and model training.
Hands-on experience with cloud-based machine learning platforms such as AWS SageMaker and Databricks, showcasing scalable model development and deployment.
Demonstrated capability to develop and implement custom statistical algorithms.
Proficiency in statistical methods for identifying patterns and trends in large datasets, essential for predictive maintenance.
Experience optimising code for performance in high-throughput, low-latency environments. Experience deploying models into production, with expertise in monitoring their performance and integrating them with CI/CD pipelines using AWS, Docker, or Kubernetes.
Familiarity with end-to-end analytical architectures, including data lakes, data warehouses, and real-time processing systems.
Experience creating insightful dashboards and reports using tools such as Power BI, Tableau, Incorta or custom visualisation frameworks to effectively communicate model results to stakeholders.
5+ years of experience in data science and explored Gen AI/DL/AIML model using ML Flows

Qualifications

Bachelor’s or Master’s degree/ Diploma in Engineering, Statistics, Mathematics or Computer Science
3+ years of experience as a Data Engineer/Data Science
Strong problem-solving skills
Proven ability to work independently and accurately
Strong Expertise in Python, Spark, R, SQL or any other programming language
Strong hands-on expertise with at least one analytics tools like Snowflake, Databricks or Incorta.