Understanding the Role of a Data Engineer | IABAC

A data engineer designs, builds, and maintains the infrastructure and tools needed to collect, store, and process large datasets. They ensure data is organized, reliable, and accessible for analysis, supporting business decision-making and data-driven projects.

1. Understanding the Role
of a Data Engineer
www.iabac.org

2. Introduction to Data Engineering
Key Responsibilities
Skills Required
Challenges Faced
Future of Data Engineering
•
•
•
•
•
Agenda
www.iabac.org

3. Introduction to Data Engineering
●
●
●
Data engineering involves designing, building, and maintaining systems and
architecture for collecting, storing, and analyzing data.
It ensures that raw data is transformed into a useful format for data scientists
and analysts to use in decision-making processes.
Data engineering is crucial for enabling organizations to leverage big data,
ensuring data quality, and supporting data-driven strategies.
Understanding Data Engineering
www.iabac.org

4. Key Responsibilities
Data Collection
Gathering data from various sources including databases, APIs, and real-time
streams.
Cleaning, enriching, and transforming raw data into usable formats for
analysis.
Designing and managing scalable storage solutions to ensure data availability
and reliability.
Data Storage
Data Transformation
www.iabac.org

5. Skills Required
Experience with ETL tools like Apache NiFi, data warehousing solutions like
Amazon Redshift, and big data technologies like Hadoop and Spark is crucial.
Strong problem-solving abilities, effective communication, and a collaborative
mindset are vital for working efficiently with cross-functional teams.
Proficiency in SQL, Python, and Java is essential. SQL for data querying, Python
for scripting and data manipulation, and Java for building robust data pipelines.
Soft Skills
Tools and
Technologies
Programming
Languages
www.iabac.org

6. Challenges Faced
Ensuring accuracy, completeness, and
consistency of data from diverse
sources.
Managing growing data volumes and
maintaining performance with efficient
infrastructure.
Combining data from different sources
while resolving format and semantics
discrepancies.
Data Quality Scalability Data Integration
www.iabac.org

7. Future of Data Engineering
Emerging Trends and Evolution
●
●
●
●
●
Adoption of AI and Machine Learning: Data engineers will increasingly integrate AI and ML
technologies to automate data processing tasks, enhance predictive analytics, and drive
data-driven decision-making.
Expansion of Data Governance and Privacy: With growing concerns over data privacy, data
engineers will need to implement robust data governance frameworks and comply with
regulations like GDPR and CCPA.
Growth of Real-Time Data Processing: The demand for real-time data insights will push data
engineers to focus on streaming data architectures and technologies like Apache Kafka and
Apache Flink.
Emphasis on Data Quality and Integration: Ensuring high-quality data and seamless integration
from various sources will remain a critical focus, requiring advanced ETL tools and techniques.
Shift to Cloud-Based Solutions: The migration to cloud platforms such as AWS, Azure, and Google
Cloud will continue to accelerate, offering scalable and flexible data engineering environments.
www.iabac.org

8. www.iabac.org
Thank you