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flow engineer

title: 'Flow Engineer: Architecting the Future of Data Movement'
salary: $110,000 - $175,000
categories: [Data Engineering, Technology, Software Development, Big Data]
description: A comprehensive overview of the key responsibilities, required technical skills and professional background for the role of a 'Flow Engineer: Architecting the Future of Data Movement'.

🎯 Role Definition

As a Flow Engineer, you are the master architect of our data ecosystem's movement and transformation. You will take ownership of the entire data lifecycle, from ingestion and processing to storage and accessibility. This role is pivotal in ensuring our data is not just available, but also timely, reliable, and pristine. You'll work at the intersection of data engineering, software development, and DevOps to build and maintain the critical infrastructure that enables data-driven decision-making across the entire company. If you are passionate about solving complex data challenges and building high-performance systems, this is the opportunity for you to make a significant impact.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Engineer / Junior Data Engineer
  • Software Engineer (with a data focus)
  • Data Analyst (with strong programming and ETL skills)

Advancement To:

  • Senior or Lead Flow Engineer
  • Data Architect
  • Data Engineering Manager

Lateral Moves:

  • MLOps Engineer
  • Platform Engineer
  • DevOps Engineer

Core Responsibilities

Primary Functions

  • Design, develop, and maintain robust, scalable, and high-performance data pipelines, implementing complex ETL and ELT processes to ingest and transform data from a wide variety of source systems.
  • Architect and manage real-time data streaming solutions using technologies like Apache Kafka, Kinesis, or Pub/Sub to support mission-critical, low-latency applications.
  • Develop and orchestrate complex data workflows and job schedules using tools such as Apache Airflow, Dagster, or Prefect, ensuring reliability and operational excellence.
  • Build and optimize data models within our data warehouse (e.g., Snowflake, BigQuery, Redshift) to support efficient analytics and business intelligence reporting.
  • Implement comprehensive data quality frameworks and validation checks throughout our pipelines to ensure the accuracy, completeness, and integrity of our data assets.
  • Monitor, troubleshoot, and debug production data pipelines, performing root cause analysis and implementing preventative measures to minimize downtime and data-related incidents.
  • Write high-quality, maintainable, and well-tested code, primarily in Python and SQL, adhering to software engineering best practices and participating in rigorous code reviews.
  • Automate the deployment and management of data infrastructure using Infrastructure as Code (IaC) principles with tools like Terraform or CloudFormation.
  • Optimize the performance and cost-efficiency of data processing jobs and queries, leveraging distributed computing frameworks like Apache Spark or Flink.
  • Collaborate closely with data scientists, analysts, and product managers to understand their data requirements and deliver effective, scalable data solutions.
  • Implement and enforce data governance and security policies, ensuring data is handled responsibly and in compliance with regulations like GDPR and CCPA.
  • Create and maintain comprehensive documentation for data pipelines, architectures, and processes to foster knowledge sharing and streamline onboarding.
  • Evaluate, prototype, and recommend new data technologies, tools, and methodologies to continuously improve our data platform and engineering practices.
  • Containerize data applications and services using Docker and manage their deployment and scaling with orchestration platforms like Kubernetes.
  • Develop reusable frameworks, libraries, and components to accelerate the development and standardization of data engineering solutions across the organization.
  • Profile data pipelines to identify and resolve performance bottlenecks, ensuring our systems can scale to handle increasing data volumes and complexity.
  • Manage and evolve our data lake and data warehouse schemas to accommodate new data sources and changing business requirements.
  • Establish and maintain CI/CD pipelines for our data engineering projects to automate testing and deployment, improving development velocity and reliability.
  • Act as a subject matter expert on data flow and pipeline architecture, providing guidance and mentorship to other engineers and stakeholders.
  • Participate in an on-call rotation to provide operational support for critical data infrastructure and pipelines, ensuring high availability and rapid incident response.
  • Develop and maintain metadata management systems and data catalogs to improve data discovery, lineage tracking, and overall data usability for the organization.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis to assist business stakeholders with urgent inquiries.
  • Contribute to the organization's overarching data strategy and long-term technology roadmap.
  • Collaborate with business units to translate complex data needs and business logic into concrete engineering requirements.
  • Participate actively in sprint planning, daily stand-ups, and retrospective ceremonies within the agile data engineering team.
  • Mentor junior engineers and analysts, sharing best practices in data engineering, coding standards, and system design.

Required Skills & Competencies

Hard Skills (Technical)

  • Expert-Level Programming: Advanced proficiency in Python and expert-level SQL for complex data manipulation, aggregation, and querying.
  • Data Orchestration: Hands-on experience building and managing complex workflows with tools like Apache Airflow, Dagster, or Prefect.
  • Big Data Technologies: Deep knowledge of distributed data processing frameworks such as Apache Spark and/or Apache Flink.
  • Streaming Platforms: Proven experience with real-time data streaming technologies like Apache Kafka, AWS Kinesis, or Google Cloud Pub/Sub.
  • Cloud Computing: Extensive experience with at least one major cloud provider (AWS, GCP, or Azure) and their core data services (e.g., S3, Glue, EMR, BigQuery, Dataflow, Azure Data Factory).
  • Data Warehousing: In-depth experience designing and working with modern cloud data warehouses like Snowflake, Google BigQuery, or Amazon Redshift.
  • Containerization & Orchestration: Proficiency with Docker for containerizing applications and Kubernetes for orchestration.
  • Infrastructure as Code (IaC): Practical experience with tools like Terraform or AWS CloudFormation to automate infrastructure provisioning.
  • Data Modeling & Warehousing Concepts: Strong understanding of data modeling techniques (e.g., Kimball, Inmon), dimensional modeling, and data warehousing principles.
  • CI/CD & DevOps: Familiarity with CI/CD principles and tools (e.g., Jenkins, GitLab CI) for automating data pipeline deployments.
  • Version Control: Mastery of Git for collaborative development and version control.

Soft Skills

  • Analytical Problem-Solving: A systematic and logical approach to identifying, analyzing, and resolving complex technical issues.
  • Strong Ownership: A proactive mindset with a high sense of accountability for the end-to-end lifecycle of data systems.
  • Effective Communication: The ability to clearly articulate complex technical concepts to both technical and non-technical audiences.
  • Collaborative Spirit: A team player who thrives in a collaborative environment and is skilled at building strong working relationships.
  • Attention to Detail: Meticulous and thorough in your work, with a passion for ensuring data accuracy and quality.
  • Adaptability & Eagerness to Learn: A curious and flexible mindset, with a strong desire to stay current with emerging technologies and continuously improve your skills.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's Degree in a quantitative or technical field.

Preferred Education:

  • Master’s Degree in a related field.

Relevant Fields of Study:

  • Computer Science
  • Software Engineering
  • Information Systems
  • Statistics or a related quantitative discipline

Experience Requirements

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

Preferred:

  • Proven track record of designing, building, and deploying production-grade data pipelines in a cloud environment.
  • Experience working in a high-volume, high-velocity data environment.
  • Demonstrable experience optimizing the performance and cost of large-scale data systems.
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