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Key Responsibilities and Required Skills for an Incremental Engineer

💰 $120,000 - $180,000

Data EngineeringSoftware DevelopmentCloud ComputingIT

🎯 Role Definition

As an Incremental Engineer, you will be a cornerstone of our data organization, focusing on the continuous and iterative improvement of our data ecosystem. You are not just building data pipelines; you are methodically enhancing, optimizing, and scaling our data infrastructure to meet the evolving needs of the business. This role requires a blend of strong data engineering fundamentals and an agile, product-oriented mindset. You will work closely with cross-functional teams to deliver value in small, consistent increments, ensuring our data platform is always robust, efficient, and aligned with our strategic goals.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Software Engineer (with a data focus)
  • Data Analyst (with strong technical/scripting skills)
  • Junior Data Engineer / ETL Developer

Advancement To:

  • Senior Incremental / Data Engineer
  • Data Architect
  • Data Engineering Manager

Lateral Moves:

  • DevOps Engineer
  • Machine Learning Engineer
  • Senior Data Scientist

Core Responsibilities

Primary Functions

  • Incrementally design, build, and operationalize robust, scalable, and high-performance data pipelines to process and integrate large, complex datasets from disparate sources like APIs, databases, and streaming platforms.
  • Continuously refactor and optimize existing data models, ETL/ELT processes, and data warehouse structures to improve efficiency, reduce latency, and lower operational costs.
  • Develop and maintain data quality frameworks and automated testing to monitor and ensure the accuracy, completeness, and consistency of data as it flows through our systems.
  • Implement and manage CI/CD (Continuous Integration/Continuous Deployment) pipelines for data infrastructure, enabling rapid, iterative deployment of changes and ensuring code quality.
  • Collaborate with data scientists and analysts to iteratively develop data products and features, ensuring the underlying data structures support their analytical and modeling needs.
  • Systematically automate manual data processes and workflows to enhance the team's productivity and reduce the potential for human error.
  • Design and implement data security and governance policies within the data platform, ensuring compliance with industry regulations and best practices.
  • Champion an agile, iterative approach to data engineering, breaking down large projects into smaller, manageable tasks that deliver incremental value.
  • Evaluate and prototype emerging data technologies and tools, providing recommendations for their adoption to continuously improve our data stack.
  • Create and maintain comprehensive documentation for data pipelines, schemas, and processes to foster a shared understanding across the organization.
  • Monitor, troubleshoot, and resolve issues in our data pipelines and infrastructure, implementing proactive alerting to minimize downtime and data-related incidents.
  • Participate in code reviews to ensure all data engineering solutions are of high quality, maintainable, and adhere to established coding standards.
  • Develop solutions for data ingestion and integration, ensuring that new data sources are added to our ecosystem in a controlled, versioned, and scalable manner.
  • Build and maintain the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and cloud-native 'big data' technologies.
  • Work with stakeholders including the Executive, Product, Data, and Design teams to assist with data-related technical issues and support their data infrastructure needs.
  • Fine-tune data processing and query performance by partitioning, indexing, and denormalizing data within our data warehouse and data lake.
  • Manage and optimize cloud data resources (e.g., compute clusters, storage, databases) to ensure cost-effectiveness without sacrificing performance or scalability.
  • Translate complex business requirements into technical specifications and architectural designs for new data systems and features.
  • Develop and maintain data catalogs and metadata management systems to make data more discoverable, understandable, and trustworthy for all users.
  • Drive the migration of legacy data systems to modern, cloud-based architectures through a phased, incremental approach that minimizes disruption to business operations.
  • Mentor junior engineers and analysts on best practices for data engineering, software development, and iterative problem-solving.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis to answer critical business questions.
  • Contribute to the organization's data strategy and roadmap by identifying opportunities for technical and process improvements.
  • Collaborate with business units to translate data needs into engineering requirements and actionable development tickets.
  • Participate in sprint planning, daily stand-ups, and retrospective agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

  • Expert-level proficiency in SQL for complex querying, data manipulation, and performance tuning.
  • Strong programming skills in Python, particularly with data manipulation libraries (e.g., Pandas, Polars) and data engineering frameworks.
  • Hands-on experience with major cloud platforms such as AWS (S3, Redshift, Glue, EMR), GCP (BigQuery, Dataflow, Composer), or Azure (Data Factory, Synapse).
  • Deep understanding of data warehousing concepts and experience with modern platforms like Snowflake, BigQuery, or Redshift.
  • Experience building and orchestrating data pipelines using tools like Apache Airflow, Prefect, or Dagster.
  • Proficiency with big data technologies such as Apache Spark for large-scale data processing.
  • Solid understanding of data modeling techniques, including dimensional modeling (star/snowflake schemas) and data vault methodologies.
  • Familiarity with CI/CD and infrastructure-as-code tools like Git, Jenkins, Terraform, and Docker.
  • Knowledge of streaming data technologies like Kafka, Kinesis, or Pub/Sub is a significant plus.

Soft Skills

  • Agile & Iterative Mindset: A natural inclination to break down complex problems and deliver value incrementally.
  • Strong Problem-Solving Skills: The ability to diagnose, analyze, and resolve complex technical issues in a data-driven manner.
  • Collaboration and Teamwork: Excellent interpersonal skills and a proven ability to work effectively with both technical and non-technical stakeholders.
  • Ownership and Accountability: A proactive, self-starting attitude with a strong sense of responsibility for the quality and reliability of your work.
  • Excellent Communication: The ability to clearly articulate technical concepts, designs, and decisions to a diverse audience.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Engineering, Information Systems, or another quantitative field.

Preferred Education:

  • Master's degree in Computer Science, Data Science, or a related technical discipline.

Relevant Fields of Study:

  • Computer Science
  • Software Engineering
  • Statistics & Mathematics
  • Information Systems

Experience Requirements

Typical Experience Range: 3-7 years of professional experience in a data engineering, software engineering, or related role.

Preferred:

  • Demonstrated experience designing, building, and maintaining production-level data systems in a cloud environment.
  • A proven track record of iteratively optimizing data pipelines for performance, cost, and reliability.
  • Experience working in an agile development environment and a deep appreciation for its principles.
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