People have been asking me to research data-related job titles, responsibilities, and key skills for each job. In this live article, I conducted research to answer the question. I will update this article every six months to keep it relevant.
What is in the article?
Possible data-related job titles, primary responsibilities for each job, and skill set the employee should have to fulfil the position. It is hard to scope the skill set. I recommend taking the given skills list as a must to have at least two skills in each category. Of Course, the more skills you have, the better you will perform.
What is not is not covered in this article?
This article does not cover salaries. I understand the importance of including the salary section in the research. Salary is affected by many factors, including region/country, company size, contract negotiation, employee experience, etc.
Data analyst
Description: A data analyst plays a crucial role in interpreting data, analyzing results, and providing actionable insights to support decision-making processes within an organization.
Main responsibilities
- Data Collection and Processing: Gather data from various sources, maintain data systems for accuracy, and use automated tools for data extraction.
- Data Analysis: Interpret data, identify trends, and analyze patterns to provide actionable insights and regular reports.
- Reporting and Presentation: Prepare clear, detailed reports and visualizations to communicate findings to stakeholders.
- Collaboration: Work closely with management and departments to prioritize data needs and develop practical solutions.
Key skills to have: I have listed the top five skills in each category to make the journey less boring and less stressful for people planning to take it. However, skills may vary depending on experience, academic background, etc.
- Languages: SQL, Python, R, SAS, Go.
- Tools: Excel, Tableau, Power BI, SAS, PowerPoint.
- Libraries: Spark, Hadoop, Pandas, Airflow, NumPy.
- Databases: SQL Server, MySQL, PostgreSQL, MongoDB, DB2.
- Cloud: Azure, Oracle, AWS, Snowflake, Big Query,
- Framework: Express, Vue.js, Node.js, Angular, Phoenix.
Business analyst
Description: A business analyst plays a pivotal role in bridging the gap between IT and business using data analytics to assess processes, determine requirements, and deliver data-driven recommendations and reports to executives and stakeholders.
Main responsibilities
- Requirements Gathering: Conduct interviews, workshops, and surveys to gather and document user requirements and business process descriptions.
- Analysis and Evaluation: Analyse business processes and workflows to identify areas for improvement and develop optimized solutions.
- Solution Implementation: Develop business cases, assist in implementing new processes and systems, and ensure solutions meet business needs.
- Stakeholder Management and Reporting: Collaborate with stakeholders, liaise between business and technical teams, and prepare detailed documentation and reports for various stakeholders.
Key skills to have: I have listed the top five skills in each category to make the journey less boring and less stressful for people planning to take it. However, skills may vary depending on experience, academic background, etc.
- Languages: SQL, Python, R, Go, SAS.
- Tools: Excel, Tableau, Power BI, PowerPoint, Word.
- Libraries: Spark, Hadoop, Pandas, GDPR, Airflow.
- Databases: SQL Server, MySQL, PostgreSQL, MongoDB, DB2.
- Cloud: Azure, Oracle, AWS, Snowflake, Redshift.
- Framework: Express, Phoenix, Ruby, Vue.js, Node.js.
Data Engineer
Description: A Data Engineer is responsible for designing, building, and maintaining the infrastructure and systems required for collecting, storing, and analyzing large data sets. They ensure that data pipelines are scalable, efficient, and reliable.
Main responsibilities
- Data Pipeline Development: Design, develop, and maintain scalable data pipelines and systems for efficient data collection, storage, and processing.
- Data Integration: Integrate data from various sources, ensuring data quality and consistency across different systems.
- Database Management: Build and maintain databases, data warehouses, and data lakes while monitoring and troubleshooting performance issues.
- Collaboration and Documentation: Work with data scientists and analysts to meet data needs and create technical documentation for data processes and systems.
Key skills to have: I have listed the top five skills in each category to make the journey less boring and less stressful for people planning to take it. However, skills may vary depending on experience, academic background, etc.
- Languages: SQL, Python, Java, Scala, NoSQL.
- Tools: Tableau, Power BI, SSIS, Excel, SAP.
- Libraries: Spark, Kafka, Hadoop, Airflow, PySpark.
- Databases: SQL Server, MySQL, Cassandra, PostgreSQL, MongoDB.
- Cloud: AWS, Azure, Snowflake, Data Bricks, Redshift.
- Framework: Node.js, Ruby, Express, Angular, Flask.
Data Scientist
Description: A Data Scientist applies statistical analysis, machine learning, and data mining techniques to interpret complex data and provide actionable insights to drive business decisions.
Main responsibilities
- Data Analysis: Collect, process, and analyze large datasets using statistical techniques to uncover trends, patterns, and insights.
- Model Development: Develop, implement, validate, and optimize machine learning models and algorithms to address business challenges.
- Data Visualization: Create and present data visualizations, dashboards, and reports to effectively communicate findings to stakeholders.
- Collaboration and Innovation: Work with cross-functional teams to meet data needs, collaborate on deploying models into production, and stay updated with the latest advancements in data science.
Key skills to have: I have listed the top five skills in each category to make the journey less boring and less stressful for people planning to take it. However, skills may vary depending on experience, academic background, etc.
- Languages: Python, SQL, Java, R, SAS.
- Tools: Tableau, Excel, Power BI, SAS, Word.
- Libraries: Spark, TensorFlow, PyTorch, Hadoop, Pandas.
- Databases: SQL Server, MySQL, MongoDB, PostgreSQL, Cassandra.
- Cloud: AWS, Azure, GCP, Data Bricks, Oracle.
- Framework: Flask, Express, Django, Ruby, Angular.
Data governance
Description: Data governance ensures that data is managed, used, and protected according to organizational policies and standards. A Data Governance Specialist, Officer or Manager is critical in establishing and enforcing data governance policies, ensuring data quality, and supporting regulatory compliance.
Main responsibilities
- Policy Development and Implementation: Develop, implement, and enforce data governance policies, standards, and procedures across the organization.
- Data Quality and Integrity: Establish and maintain data quality metrics, monitor data quality, and address data quality issues.
- Regulatory Compliance: Collaborating with legal and compliance teams to ensure compliance with data protection and privacy regulations.
- Collaboration and Training: Work with data stakeholders to promote data governance initiatives and provide training on policies and procedures.
Key skills to have: I have listed the top five skills to make the journey less boring and less stressful for people planning to take it. However, skills may vary depending on experience, academic background, etc.
- Strong understanding of data governance frameworks and best practices.
- Proficiency in data management tools and technologies.
- Excellent analytical, problem-solving, and communication skills.
- Ability to work collaboratively with cross-functional teams.
Data Steward
Description: A Data Steward is responsible for ensuring an organisation’s proper data management and quality. This role focuses on maintaining data integrity, enforcing data governance policies, and supporting data management practices.
Main responsibilities
- Data Quality Management: Monitor and enforce data quality standards and address data quality issues through cleansing and validation.
- Data Governance Support: Implement and adhere to data governance policies and assist in maintaining data definitions and standards.
- Data Management: Maintain data documentation, including metadata and data lineage, and ensure data management practices meet organizational requirements.
- Training and Support: Provide data users with training and support on best practices and governance policies and serve as a point of contact for data-related inquiries.
Key skills to have: I have listed the top five skills to make the journey less boring and less stressful for people planning to take it. However, skills may vary depending on experience, academic background, etc.
- Strong understanding of data governance and data management principles.
- Proficiency in data management tools and technologies.
- Excellent analytical and problem-solving skills.
- Strong communication and interpersonal skills.
Data leader
Description: A Data Leader is responsible for setting the strategic direction for an organisation’s data management, governance, and analytics. This role typically involves overseeing data strategy, ensuring data quality, and leveraging data to drive business insights and decisions.
Main responsibilities
- Develop and Execute Data Strategy: Create and implement a data strategy that aligns with organizational goals and drives the overall direction of data management and analytics.
- Oversee Data Governance and Quality: Ensure the implementation and adherence to data governance policies and maintain high data quality and integrity standards.
- Drive Data Analytics and Insights: Utilize data analytics to generate actionable business insights, support strategic decision-making, and address business challenges.
- Lead and Collaborate: Manage a team of data professionals, collaborate with cross-functional teams, and communicate data strategy and insights to senior leadership and stakeholders.
Key skills to have: I have listed the top five skills to make the journey less boring and less stressful for people planning to take it. However, skills may vary depending on experience, academic background, etc.
- Strong strategic thinking and leadership abilities.
- Proficiency in data management tools and technologies (e.g., SQL, Hadoop, Spark).
- Expertise in data analytics and visualization tools (e.g., Tableau, Power BI).
- Excellent communication, interpersonal, and problem-solving skills.