Analytics Engineer

Socket.dev; Shelton (CT); On-site; USD 100,000 - 150,000; Full time

Job summary

Socket.dev seeks an Analytics Engineer to design and maintain data pipelines, models, and analytics tools. You will apply data science techniques to build predictive models that impact manufacturing, product development, and sales strategies.

You will collaborate with engineers, product managers, and business stakeholders to define KPIs, dashboards, and reporting tools, while advancing data infrastructure and governance across the organization.

Qualifications

• Bachelor’s degree in a related field; Master’s preferred.
• 2+ years in data engineering, data science, or similar.
• Strong SQL and Python for data manipulation and analysis.
• Experience with ETL, data warehousing, cloud platforms, and ML frameworks.
• Strong data modeling, data architecture, and BI tooling knowledge.
• Familiarity with big data tech (Hadoop, Spark) is a plus.
• Experience with ML algorithms and predictive modeling.
• Excellent problem-solving and communication skills.

Responsibilities

• Design, build, and maintain scalable data pipelines for large data volumes.
• Develop data models and KPIs; create dashboards and reports.
• Apply statistical methods and data science techniques to improve manufacturing, product development, and sales.
• Develop predictive models and ML algorithms; integrate into products.
• Collaborate with engineers and product teams to deploy data science solutions.
• Provide training and support on data tools and analytics best practices.

Skills

SQL

Python

Data Engineering

Data Science

Machine Learning

Communication

Collaboration

Education

Bachelor’s in Computer Science or Data Science

Master’s degree preferred

Tools

ETL tools

Power BI/Tableau

AWS/Azure/GCP

Spark

Grafana

Job description

Position Overview:

We are seeking a skilled and motivated Analytics Engineer to join our growing Analytics team. The successful candidate will be responsible for designing, developing, and maintaining data pipelines, models, and analytics tools that drive key business decisions. Additionally, the role will involve leveraging data science techniques to build predictive models, perform statistical analysis, and develop machine learning solutions that enhance our product development, manufacturing processes, and business strategies.

Key Responsibilities:

1. Data Pipeline Development:
   • Design, build, and maintain scalable data pipelines to process and analyze large volumes of data from multiple sources.
   • Ensure data is clean, reliable, and readily available for analysis, reporting, and data science applications.
   • Optimize data workflows for performance, cost, and maintainability.
2. Data Modeling and Analysis:
   • Develop and maintain data models that reflect the company’s key business processes.
   • Work with cross-functional teams to define and implement KPIs, dashboards, and reporting tools.
   • Apply statistical methods and data science techniques to analyze complex datasets, identifying trends, patterns, and opportunities for improvement in manufacturing, product development, and sales strategies.
3. Data Science and Machine Learning:
   • Develop predictive models and machine learning algorithms to solve business problems and enhance decision-making processes.
   • Collaborate with engineers and product teams to integrate data science solutions into existing products and services.
   • Continuously explore new data science methodologies and tools to drive innovation within the company.
4. Collaboration and Support:
   • Partner with engineers, product managers, and business stakeholders to understand their data and analytics needs.
   • Provide technical support and training to team members and other departments on the use of data tools, data science, and analytics best practices.
   • Work closely with IT to ensure data infrastructure is aligned with company goals and industry best practices.
5. Continuous Improvement:
   • Identify opportunities to improve existing data processes, analytics tools, and data science methodologies.
   • Stay current with industry trends, tools, and technologies in data engineering, data science, and analytics.
   • Contribute to the development and execution of the company’s data strategy.
6. Quality Assurance:
   • Implement data validation, testing, and documentation processes to ensure the accuracy and reliability of analytics and data science outputs.
   • Ensure compliance with data governance policies and best practices.

Qualifications:

• Bachelor’s degree in Business Analytics, Computer Science, Data Engineering, Data Science, or a related field. A Master’s degree is a plus.
• 2+ years of experience in data engineering, data science, or a related role, preferably in a manufacturing environment.
• Proficiency in SQL, Python, or other programming languages for data manipulation, analysis, and data science applications.
• Experience with ETL tools, data warehousing, cloud platforms (e.g., AWS, Azure, Google Cloud), and machine learning frameworks.
• Strong understanding of data modeling, data architecture, statistical analysis, and business intelligence tools (e.g., Power BI, Tableau, Grafana).
• Familiarity with big data technologies and frameworks (e.g., Hadoop, Spark) is a plus.
• Experience with machine learning algorithms, predictive modeling, and data science tools (e.g., TensorFlow, scikit-learn).
• Excellent problem-solving skills and attention to detail.
• Strong communication and collaboration skills with the ability to translate technical concepts to non-technical stakeholders.
• Ability to manage multiple projects and prioritize tasks in a fast-paced environment.