Remote Data Engineer

Location: Remote
Salary: $115,844 per year

Job Overview:

We seek an experienced and highly motivated Data Engineer to join our remote team. The ideal candidate will be responsible for building and maintaining scalable data pipelines, developing data models, and collaborating closely with cross-functional teams such as data scientists, software engineers, and business stakeholders to ensure smooth data flow and high-quality data-driven solutions.

As a Data Engineer, you will be critical in ensuring that data systems and infrastructure are optimized for performance and scalability. The successful candidate will be responsible for designing and implementing solutions supporting efficient ingestion, processing, and analyzing large volumes of data from multiple sources. This role requires someone with a passion for data, a strong understanding of cloud technologies, and a desire to stay on the cutting edge of new data technologies and methodologies.

You will join a dynamic and forward-thinking company where data is at the heart of decision-making. You can work with various data types and systems using the latest tools and technologies. Your work will directly impact the organization’s ability to leverage data for business intelligence, predictive analytics, and machine learning initiatives.

Key Responsibilities:

• Data Pipeline Development and Maintenance
  Design, build, and maintain data pipelines to support large-scale data processing. Ensure that these pipelines are optimized for performance, scalability, and reliability. Develop custom data ingestion pipelines from multiple data sources (structured and unstructured) and ensure smooth data flow into the data warehouse.
• Data Modeling
  Collaborate with data scientists and analysts to develop efficient and scalable data models. Work closely with business stakeholders to understand and translate their data needs into robust data models supporting data-driven decisions.
• Database Design and Optimization
  Design and implement data storage solutions optimized for performance, security, and scalability. Ensure that databases are designed to handle large volumes of data and can be easily scaled as data needs grow.
• Data Integration
  Integrate data from a wide range of internal and external data sources. Ensure the data is cleaned, transformed, and ready for analysis by the data science and analytics teams.
• Data Quality Assurance
  Implement automated data quality checks to ensure accuracy, completeness, and consistency throughout the data pipeline. Monitor and troubleshoot data issues and work with the appropriate teams to resolve them promptly.
• Collaboration and Communication
  I work closely with various teams, including software engineers, data scientists, product managers, and business analysts, to understand their data requirements and provide solutions that meet their needs. I also communicate technical concepts to non-technical stakeholders.
• Cloud Infrastructure Management
  Build and maintain data infrastructure on cloud platforms (AWS, Google Cloud, Azure, etc.). Implement scalable cloud-based solutions to handle data processing workloads and ensure that cloud resources are optimized for cost and performance.
• Security and Compliance
  Ensure all data engineering processes comply with relevant data security and privacy regulations. Implement data encryption, access controls, and other security measures to protect sensitive data.
• Documentation
  Develop and maintain detailed documentation for all data pipelines, models, and storage systems. Ensure that other teams can easily understand and work with the data infrastructure.

Qualifications:

• Educational Background
  A Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or a related field.
• Experience
  A minimum of 3-5 years of experience in data engineering or a related field is required. Hands-on experience building scalable data pipelines, data warehousing, and data modeling is essential.
• Technical Skills
  • Programming Languages: Proficiency in Python, Java, or Scala for data processing tasks.
  • Database Management: Strong knowledge of SQL and NoSQL databases (PostgreSQL, MySQL, MongoDB, etc.).
  • Data Warehousing: Experience with data warehousing technologies such as Snowflake, Redshift, or BigQuery.
  • ETL Tools: Experience with ETL tools such as Apache Airflow, Talend, or similar.
  • Cloud Technologies: Hands-on experience with cloud platforms like AWS, GCP, or Azure. Familiarity with tools such as S3, Lambda, or Dataproc.
  • Big Data Technologies: Knowledge of Hadoop, Spark, and Kafka for processing large datasets is a plus.
  • Data Visualization: Familiarity with data visualization tools like Tableau, Power BI, or Looker is a plus.
• Soft Skills
  • Strong problem-solving skills and the ability to work independently.
  • Excellent verbal and written communication skills.
  • Proven ability to collaborate with cross-functional teams and stakeholders.
  • Ability to manage multiple projects and deadlines in a fast-paced environment.