Key Responsibilities and Required Skills for a Honey Extractor (Data Engineer)

🎯 Role Definition

The Honey Extractor (Data Engineer) is a pivotal member of our technology team, specializing in the design, construction, and maintenance of our large-scale data processing systems. You are the architect of the data hive, ensuring that every drop of information is collected, cleaned, and made available for consumption by analysts, data scientists, and business leaders. Your work is the foundation of our data-driven culture, enabling the entire organization to make smarter, faster decisions. You're not just moving data; you're extracting value and building the infrastructure for intelligence.

📈 Career Progression

Typical Career Path

Entry Point From:

Data Analyst
Software Engineer
BI Developer
Database Administrator

Advancement To:

Senior Data Engineer
Staff Data Engineer
Data Architect
Analytics Engineering Manager

Lateral Moves:

Analytics Engineer
Machine Learning Engineer

Core Responsibilities

Primary Functions

Design, build, and maintain scalable and resilient ETL/ELT data pipelines to collect, process, and transport data from a wide variety of source systems.
Develop and implement robust data models and database schemas within our cloud data warehouse to support complex business intelligence and analytics use cases.
Architect and implement resilient data ingestion frameworks for both batch and real-time data streams, ensuring data is fresh and available when needed.
Write complex, highly-performant, and readable SQL queries to transform raw data into curated, analysis-ready datasets for stakeholders across the company.
Implement comprehensive data quality checks, validation rules, and automated monitoring systems to ensure the accuracy, completeness, and integrity of our data assets.
Manage and optimize our data orchestration tools, such as Apache Airflow or Dagster, to ensure the reliable and efficient execution of thousands of daily data jobs.
Collaborate with software engineers to integrate new data sources from production applications, ensuring data contracts are well-defined and rigorously maintained.
Work closely with data analysts and data scientists to deeply understand their data requirements and build data products that empower their research and reporting.
Proactively identify and automate manual data processes to improve efficiency, reduce the potential for human error, and free up the team for higher-value tasks.
Systematically profile and analyze source data to identify potential issues, anomalies, and opportunities for enrichment before they impact downstream consumers.
Own the end-to-end lifecycle of critical data pipelines, from initial requirements gathering and design to deployment, monitoring, and ongoing maintenance.
Implement and enforce data governance and security best practices to protect sensitive information and ensure compliance with regulations like GDPR and CCPA.
Optimize the performance and cost of our data infrastructure by fine-tuning queries, partitioning tables, and leveraging cloud-native features effectively.
Create and maintain thorough, user-friendly documentation for data pipelines, data models, and business logic to serve as a single source of truth.
Develop custom data connectors and scripts (primarily in Python) to pull data from third-party APIs and other non-standard or challenging sources.
Manage the deployment of data infrastructure using Infrastructure as Code (IaC) principles with tools like Terraform or CloudFormation.
Evaluate, prototype, and recommend new data technologies, tools, and frameworks that can enhance our data platform's capabilities and efficiency.
Troubleshoot and resolve complex data-related issues and production incidents in a timely manner, performing root cause analysis to prevent recurrence.
Build and maintain curated "golden" datasets that serve as the canonical source for key business metrics and KPIs, ensuring consistency across all reporting.
Mentor junior engineers and analysts, sharing best practices in data engineering, SQL optimization, and effective software development principles.
Participate in peer code reviews to ensure all data engineering work adheres to high standards of quality, readability, and performance.
Partner with the platform engineering team to manage and scale the underlying computational and storage resources for the data ecosystem.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis to answer urgent business questions.
Contribute to the organization's long-term data strategy and technical roadmap.
Collaborate with business units to translate ambiguous data needs into concrete engineering requirements.
Participate in sprint planning, retrospectives, and other agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

Expert-Level SQL: Deep proficiency in writing complex, optimized SQL queries, including window functions, CTEs, and query tuning across different database engines.
Programming Proficiency: Strong coding skills in a language like Python, Scala, or Java for data manipulation, automation, and pipeline development.
ETL/ELT Orchestration: Hands-on experience with workflow management tools such as Apache Airflow, Dagster, Prefect, or similar.
Cloud Data Platforms: Proven experience working with at least one major cloud provider's data stack (AWS - Redshift/S3/Glue; GCP - BigQuery/Cloud Storage/Dataflow; Azure - Synapse/Data Lake/Data Factory).
Modern Data Warehousing: Deep knowledge of columnar data warehouses like Snowflake, BigQuery, or Redshift, including data modeling and performance optimization.
Data Modeling: Strong understanding of data modeling concepts, including dimensional modeling (star/snowflake schemas) and data vault methodologies.
Big Data Technologies: Familiarity with distributed computing frameworks like Apache Spark for processing large-scale datasets.
Version Control & CI/CD: Proficient use of Git for version control and experience with CI/CD practices for deploying data pipelines.
Data Transformation Tools: Experience with modern data transformation tools, especially dbt (data build tool), is highly desirable.
Containerization: Knowledge of Docker and container orchestration systems like Kubernetes is a significant plus.

Soft Skills

Systematic Problem-Solving: An analytical mindset with the ability to deconstruct complex problems and implement durable, scalable solutions.
Strong Communication: The ability to clearly articulate technical concepts and data-related insights to both technical and non-technical audiences.
Collaboration & Teamwork: A proactive team player who thrives in a collaborative environment, working effectively with engineers, analysts, and business partners.
High Degree of Ownership: A strong sense of responsibility for the quality and reliability of the data platform, from development through production.
Business Acumen: The ability to understand business objectives and translate them into technical requirements and impactful data solutions.
Meticulous Attention to Detail: A commitment to data accuracy and quality, with a keen eye for spotting inconsistencies and potential issues.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in a quantitative or technical field, or equivalent hands-on professional experience in a data-centric role.

Preferred Education:

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

Relevant Fields of Study:

Computer Science
Information Systems
Engineering
Mathematics

Experience Requirements

Typical Experience Range: 3+ years of professional experience in a data engineering, backend software engineering, or business intelligence role.

Preferred: We particularly value experience building data platforms from the ground up, working with terabyte-scale datasets, and contributing to a culture of data excellence.