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Key Responsibilities and Required Skills for an Extraction Specialist

💰 $75,000 - $120,000

Data EngineeringITData ScienceBusiness Intelligence

🎯 Role Definition

As an Extraction Specialist, you are the architect and guardian of our data ingestion pipelines. You are a highly technical professional responsible for identifying, retrieving, and channeling vast amounts of data from diverse internal and external sources into our central data ecosystem. This role is critical for ensuring that the data powering our analytics, machine learning models, and strategic business decisions is accurate, timely, and accessible. You will tackle challenges ranging from complex API integrations and database queries to sophisticated web scraping, ensuring the seamless flow of information that is the lifeblood of our data-driven organization.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst
  • Junior Software Developer
  • Database Administrator (DBA)

Advancement To:

  • Senior Data Engineer
  • Data Architect
  • Analytics Engineering Manager

Lateral Moves:

  • Business Intelligence (BI) Developer
  • Data Scientist
  • Machine Learning Engineer

Core Responsibilities

Primary Functions

  • Design, develop, and maintain robust, scalable, and efficient ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) pipelines to ingest data from a wide variety of sources.
  • Implement and manage complex web scraping and crawling solutions using frameworks like Scrapy, Selenium, or Playwright to gather publicly available data from dynamic websites.
  • Develop custom scripts and applications for automated data extraction from third-party APIs (REST, GraphQL, SOAP), ensuring efficient handling of authentication, rate limits, and pagination.
  • Write advanced, performance-optimized SQL queries to extract data from various relational databases (e.g., PostgreSQL, MySQL, SQL Server) and data warehouses (e.g., Snowflake, Redshift, BigQuery).
  • Interface with NoSQL databases (e.g., MongoDB, Cassandra) to extract and process semi-structured and unstructured data for analytical purposes.
  • Implement comprehensive data quality checks, validation rules, and cleansing procedures at the point of extraction to ensure the integrity and accuracy of incoming data.
  • Monitor, troubleshoot, and debug data extraction processes and pipelines, proactively identifying and resolving issues to minimize data latency and ensure high availability.
  • Automate data pipeline orchestration and scheduling using tools like Apache Airflow, Prefect, Dagster, or cloud-native services to ensure reliable, hands-off operation.
  • Document all data extraction processes, including data sources, data dictionaries, transformation logic, API endpoints, and pipeline architecture for knowledge sharing and maintainability.
  • Manage the extraction of data from unstructured file formats such as PDFs, text documents, and images, potentially utilizing OCR (Optical Character Recognition) and NLP techniques.
  • Collaborate with cloud engineers to deploy and manage data extraction workflows on cloud platforms (AWS, Azure, GCP), leveraging services like AWS Glue, Lambda, Azure Data Factory, or Google Cloud Functions.
  • Develop strategies for handling large-scale data ingestion, applying principles of distributed computing and parallel processing to optimize for speed and cost.
  • Ensure all data extraction and handling activities are compliant with data governance policies and security standards, including PII protection and adherence to regulations like GDPR or CCPA.
    Ä°mplement version control for all extraction scripts and infrastructure-as-code configurations using Git, following CI/CD best practices for deployment.
  • Reverse engineer data formats and sources where documentation is lacking to enable successful data retrieval.
  • Evaluate and recommend new technologies, tools, and approaches for data extraction to continuously improve the efficiency and capability of the data platform.
  • Profile and analyze source data systems to identify key entities, relationships, and attributes necessary for extraction.
  • Create and manage connectors to SaaS platforms (e.g., Salesforce, HubSpot, Google Analytics) to centralize business application data.
  • Develop error handling and alerting mechanisms to quickly notify stakeholders of any failures or anomalies in the data extraction pipeline.
  • Optimize data transfer and storage costs by implementing efficient data compression, serialization formats (e.g., Parquet, Avro), and partitioning strategies.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis to assist business analysts and data scientists.
  • Contribute to the organization's overall data strategy and data architecture roadmap.
  • Collaborate with various business units to translate their data needs into technical engineering requirements.
  • Participate in sprint planning, daily stand-ups, and other agile ceremonies within the data engineering team.
  • Mentor junior data engineers and analysts on best practices for data extraction and pipeline development.
  • Create dashboards to monitor the health, performance, and data quality of extraction pipelines.

Required Skills & Competencies

Hard Skills (Technical)

  • Programming Languages: High proficiency in Python (with libraries like Pandas, Requests, SQLAlchemy) and expert-level SQL.
  • ETL/ELT Frameworks: Hands-on experience with Apache Airflow, Prefect, dbt, or similar orchestration and transformation tools.
  • Web Scraping: Deep knowledge of web scraping tools and frameworks such as Scrapy, BeautifulSoup, Selenium, or Playwright.
  • API Integration: Expertise in working with RESTful and other APIs, including handling authentication (OAuth, API Keys).
  • Cloud Platforms: Experience with at least one major cloud provider (AWS, GCP, Azure) and their data services (e.g., S3, Glue, Lambda, BigQuery, Azure Data Factory).
  • Database Systems: Strong understanding of both relational (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis) databases.
  • Containerization: Familiarity with Docker for creating consistent, portable development and production environments.
  • Version Control: Proficient use of Git for code collaboration and versioning.
  • Data Warehousing: Experience with cloud data warehouses like Snowflake, BigQuery, or Redshift.
  • Linux/Shell Scripting: Comfortable working in a command-line environment and writing shell scripts for automation.

Soft Skills

  • Problem-Solving: An analytical mindset with the ability to deconstruct complex technical challenges and find effective solutions.
  • Attention to Detail: Meticulous and detail-oriented, especially concerning data quality, accuracy, and consistency.
  • Strong Communication: Ability to clearly explain complex technical concepts to both technical and non-technical stakeholders.
  • Adaptability: Capable of quickly learning new technologies and adapting to evolving data sources and business requirements.
  • Autonomy & Ownership: A self-starter who can manage projects independently and takes full ownership of their work from conception to completion.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master's degree in a relevant field.

Relevant Fields of Study:

  • Computer Science
  • Information Systems
  • Data Engineering
  • Statistics or a related quantitative field

Experience Requirements

Typical Experience Range: 3-7 years of hands-on experience in a data engineering, ETL development, or similar role.

Preferred:

  • Proven experience building and maintaining production-grade data pipelines.
  • A portfolio of projects (e.g., on GitHub) demonstrating expertise in data extraction, web scraping, or API integration.
  • Experience working in an agile software development environment.
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