Senior Data Engineer

3 Months ago • 5-7 Years • Data Analyst

Job Summary

Job Description

Seeking Alpha seeks a Senior Data Engineer to build and maintain data pipelines using Spark, Scala, and Python, with AWS experience and expertise in data warehousing concepts. Strong problem-solving skills and communication skills are essential.
Must have:
  • Spark, Scala, Python
  • AWS services
  • Data warehousing
  • Data pipeline
Good to have:
  • Vertica or Redshift
  • Machine learning
  • Data governance
  • Spark internals
Perks:
  • Work-life balance
  • Continuous training

Job Details

Description

Seeking Alpha is looking for a talented and experienced Senior Data Engineer to join us.

In this role, you will design, build, and maintain the infrastructure required to analyze large data sets. As an expert in data management, ETL (extract, transform, load) processes, and data warehousing, you will work with various big data technologies such as Spark, Hadoop, and NoSQL databases.

Beyond your technical expertise, you will need strong communication and collaboration skills. You will work closely with the data and analytics team and other stakeholders to identify and prioritize data engineering projects, ensuring the data infrastructure aligns with overall business goals and objectives.

Why we’re a great company to work for

Seeking Alpha is the leading online destination for engaged investors. We have an awesome product. Our crowdsourced research and cutting-edge investing tools are helping nearly 300,000 paying subscribers exceed their financial goals.

We care about work-life balance: We work primarily from home, provide lots of perks, and insist that you enjoy them.

We invest in people. We consider each employee a long-term investment, and we see value in continuously nurturing and training our teammates.

If that's what you're also looking for, go ahead and apply!

Responsibilities

  • Work closely with data scientists/analytics and other stakeholders to identify and prioritize data engineering projects and to ensure that the data infrastructure is aligned with business goals and objectives
  • Design, build and maintain optimal data pipeline architecture for extraction, transformation, and loading of data from a wide variety of data sources, including external APIs, data streams, and data stores. 
  • Continuously monitor and optimize the performance and reliability of the data infrastructure, and identify and implement solutions to improve scalability, efficiency, and security
  • Stay up-to-date with the latest trends and developments in the field of data engineering, and leverage this knowledge to identify opportunities for improvement and innovation within the organization
  • Solve challenging problems in a fast-paced and evolving environment while maintaining uncompromising quality.
  • Implement data privacy and security requirements to ensure solutions comply with security standards and frameworks.
  • Enhance the team's dev-ops capabilities.

Requirements

  • Bachelor's or Master's degree in Computer Science, Engineering, or a related field
  • 2+ years of proven experience developing large-scale software using an object-oriented or a functional language.
  • 5+ years of professional experience in data engineering, focusing on building and maintaining data pipelines and data warehouses
  • Strong experience with Spark, Scala, and Python, including the ability to write high-performance, maintainable code
  • Experience with AWS services, including EC2, S3, Athena, Lambda and EMR
  • Familiarity with data warehousing concepts and technologies, such as columnar storage, data lakes, and SQL
  • Experience with data pipeline orchestration and scheduling using tools such as Airflow
  • Strong problem-solving skills and the ability to work independently as well as part of a team
  • High-level English - a must
  • A team player with excellent collaboration skills.

         Nice to Have:

  • Expertise with Vertica or Redshift, including experience with query optimization and performance tuning
  • Experience with machine learning and/or data science projects
  • Knowledge of data governance and security best practices, including data privacy regulations such as GDPR and CCPA.
  • Knowledge of Spark internals (tuning, query optimization)

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