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Key Responsibilities and Required Skills for Extraction Analyst

💰 $75,000 - $115,000

Data & AnalyticsData EngineeringBusiness IntelligenceTechnology

🎯 Role Definition

As a pivotal member of our data organization, the Extraction Analyst serves as the architect and guardian of our data acquisition strategy. You are at the forefront of our data-driven culture, responsible for the art and science of identifying, collecting, and channeling data from a vast and varied landscape of sources. Your core mission is to engineer and manage robust, scalable, and highly reliable data extraction and ingestion pipelines.

This role requires a tenacious problem-solver with a meticulous eye for detail and a deep understanding of data systems. You will be the crucial link between raw, disparate data and the actionable insights that drive our business forward. If you thrive on the challenge of taming complex data streams and transforming them into pristine, analytics-ready assets, this is the role for you.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Data Analyst
  • Data Technician
  • Business Systems Analyst
  • Database Developer

Advancement To:

  • Senior Extraction Analyst / ETL Lead
  • Data Engineer
  • Data Architect
  • Business Intelligence (BI) Developer

Lateral Moves:

  • Data Scientist
  • Business Intelligence Analyst
  • Database Administrator (DBA)

Core Responsibilities

Primary Functions

  • Design, develop, and maintain robust, scalable ETL (Extract, Transform, Load) and ELT processes to acquire data from a wide variety of sources, including relational databases, legacy systems, and flat files.
  • Author and optimize complex, high-performance SQL queries for large-scale data extraction, manipulation, and validation across various database platforms like SQL Server, PostgreSQL, and Oracle.
  • Develop, deploy, and manage automated data extraction solutions and scripts using Python (with libraries like Pandas, Requests, SQLAlchemy) to streamline data collection and processing.
  • Perform comprehensive data profiling and deep-dive analysis on source systems to thoroughly understand data structures, interdependencies, quality issues, and business logic.
  • Monitor, debug, and troubleshoot production data extraction jobs and pipelines to ensure high availability, data integrity, and optimal performance, implementing proactive alerting mechanisms.
  • Implement and enforce rigorous data quality checks, cleansing routines, and validation rules within the extraction layer to identify and remediate data anomalies, inconsistencies, and missing values.
  • Build and maintain sophisticated web scraping and crawling solutions using tools like Scrapy or BeautifulSoup to gather data from public web sources, while adhering to ethical standards and terms of service.
  • Develop and manage integrations with third-party vendor APIs (REST/SOAP) to extract critical business data, handling complex authentication, pagination, rate limiting, and error-handling scenarios.
  • Create and maintain detailed documentation for all data extraction processes, including data source lineage, data dictionaries, transformation logic, and operational runbooks.
  • Partner closely with data engineers to ensure extracted data is modeled and loaded correctly into the target data warehouse or data lake.
  • Translate complex business requirements from stakeholders into detailed technical specifications for data extraction and integration tasks.
  • Perform root cause analysis on data quality issues, collaborating with source system owners and business users to implement lasting corrective and preventive actions.
  • Evaluate, prototype, and recommend new data extraction tools, technologies, and methodologies to continuously improve the efficiency and capability of the data platform.
  • Ensure all data extraction and handling processes are compliant with data governance policies and data privacy regulations such as GDPR and CCPA.
  • Conduct performance tuning of extraction queries and ETL workflows to minimize latency and reduce resource consumption on source systems and ETL infrastructure.
  • Develop custom parsers and scripts to process and structure data from unstructured and semi-structured sources like JSON, XML, log files, and text documents.
  • Participate in the design and architecture of data warehousing solutions, providing expert input on the data ingestion and integration layers.
  • Build and maintain reusable code libraries and frameworks to accelerate the development of new data extraction processes.
  • Conduct thorough unit testing, integration testing, and data validation for all developed data pipelines to ensure they are accurate and function as designed.
    mobilize data from on-premise sources to cloud platforms (AWS, Azure, GCP), utilizing cloud-native services like AWS Glue or Azure Data Factory.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis to assist business stakeholders with urgent data needs.
  • Contribute to the organization's data strategy and roadmap by identifying opportunities for new data sources and process improvements.
  • Collaborate with business units to translate their evolving data needs into clear, actionable requirements for the data engineering and analytics teams.
  • Participate in sprint planning, daily stand-ups, and retrospective meetings as part of an agile development team.
  • Mentor junior analysts and team members on data extraction best practices, tools, and techniques.
  • Create and present reports on data pipeline performance, data quality metrics, and project status to technical and business leadership.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL Proficiency: The ability to write, debug, and optimize complex SQL queries, stored procedures, and functions for large-scale data extraction.
  • Strong Scripting/Programming: Expertise in a language like Python for data manipulation (Pandas), API interaction (Requests), and web scraping (BeautifulSoup, Scrapy).
  • ETL/ELT Tooling Experience: Hands-on experience with industry-standard ETL/ELT tools such as SQL Server Integration Services (SSIS), Informatica PowerCenter, Talend, or cloud-native tools like Azure Data Factory or AWS Glue.
  • Database Expertise: Deep knowledge of relational database systems (e.g., SQL Server, PostgreSQL, MySQL) and familiarity with NoSQL databases (e.g., MongoDB).
  • API Integration: Proven ability to work with and extract data from various RESTful and SOAP APIs, including handling authentication (OAuth, API Keys) and data formats (JSON, XML).
  • Data Warehousing Concepts: Solid understanding of data modeling, dimensional schemas (star, snowflake), and the principles of building and populating data warehouses.
  • Cloud Platform Familiarity: Experience with at least one major cloud provider (AWS, Azure, or GCP) and its core data services.
  • Version Control: Proficiency in using version control systems, particularly Git, for managing code and collaboration.
  • Data Quality and Governance: Knowledge of data quality frameworks, data cleansing techniques, and an understanding of data governance and privacy principles.
  • Big Data Technologies (Bonus): Familiarity with big data ecosystems, including tools like Spark, Hadoop, Kafka, or Databricks is a significant plus.

Soft Skills

  • Meticulous Attention to Detail: A precise and thorough approach to ensure data accuracy and integrity, catching issues others might miss.
  • Analytical & Problem-Solving Mindset: The ability to deconstruct complex data problems, perform root cause analysis, and implement effective solutions.
  • Excellent Communication: Capable of clearly articulating technical concepts and data findings to both technical peers and non-technical business stakeholders.
  • Resilience and Tenacity: The drive to persist through challenging technical problems and ambiguous data requirements to deliver results.
  • Strong Time Management: Excellent organizational skills to manage and prioritize multiple data extraction projects and ad-hoc requests simultaneously.

Education & Experience

Educational Background

Minimum Education:

  • A Bachelor's degree is required.

Preferred Education:

  • A Master’s degree in a quantitative or technical field is highly preferred.

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range:

  • 3-7 years of direct experience in a data-focused role such as Data Analyst, ETL Developer, or BI Developer, with a strong emphasis on data extraction and transformation.

Preferred:

  • Preference will be given to candidates with demonstrable experience building and maintaining data pipelines in a cloud environment (AWS, Azure, or GCP) and a proven track record of successfully working with large, complex datasets from multiple sources.
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