Data Engineer

Job Description

A leading organization is seeking a Data Engineer to support the development, maintenance, and optimization of enterprise data solutions that enable analytics, reporting, and data-driven decision-making. This position will work closely with data engineering, analytics, product, and business teams to deliver reliable and scalable data pipelines, support data platform initiatives, and contribute to ongoing modernization efforts.

The ideal candidate has strong SQL expertise, experience working with enterprise data warehouses such as Snowflake, and a solid foundation in data engineering principles. Exposure to large-scale data migration initiatives, particularly data warehouse modernization efforts, is highly valued.

Responsibilities

• Build, maintain, and enhance data pipelines that support analytics, reporting, and enterprise data products.
• Develop and optimize SQL-based processes for data extraction, transformation, validation, and delivery.
• Implement data quality controls, monitoring processes, and validation routines to ensure reliable data availability.
• Support pipeline automation, job scheduling, and troubleshooting activities across data environments.
• Collaborate with engineering, analytics, product, and business stakeholders to understand requirements and deliver scalable data solutions.
• Assist with data warehouse modernization and migration initiatives.
• Document data assets, metadata, pipeline workflows, and technical processes according to established standards.
• Participate in code reviews, testing activities, and deployment processes.
• Investigate and resolve data issues, escalating risks and technical challenges when appropriate.
• Contribute to continuous improvement initiatives related to data engineering practices and platform operations.

Required Experience and Skills

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related quantitative field.
• 2 to 4 years of experience in data engineering, analytics engineering, database development, data development, or a related technical discipline.
• Strong SQL skills with experience writing complex queries for data extraction, transformation, and analysis.
• Experience working with enterprise data warehouse platforms such as Snowflake.
• Working knowledge of Python or another programming language used for data processing and automation.
• Understanding of data pipeline concepts, including ingestion, transformation, orchestration, scheduling, and monitoring.
• Exposure to cloud-based data environments.
• Familiarity with data quality processes, validation frameworks, metadata management, data lineage, and access controls.
• Basic understanding of data modeling concepts, including fact tables, dimension tables, relationships, and analytical data structures.
• Experience using source control tools such as Git.
• Ability to work within Agile or iterative development environments.
• Strong analytical, problem-solving, and organizational skills.
• Excellent written and verbal communication skills.
• Ability to collaborate effectively with both technical and business stakeholders.

Preferred Experience and Skills

• Experience supporting large-scale data migration or data warehouse modernization initiatives.
• Familiarity with Snowflake implementation, optimization, or migration projects.
• Experience with Spark, PySpark, or distributed data processing technologies.
• Familiarity with orchestration tools such as Apache Airflow, Azure Data Factory, or similar platforms.
• Exposure to cloud platforms including Azure, AWS, or Google Cloud.
• Master’s degree in Computer Science or a related quantitative field.
• Demonstrated interest in expanding expertise within modern data engineering and cloud data platforms.

FAQ

1. What does a Data Engineer do?

A Data Engineer builds and maintains systems that collect, transform, store, and deliver data for analytics, reporting, applications, and machine learning. The role focuses on reliable data pipelines, integration, data quality, and efficient access to information.

2. What types of data pipelines does a Data Engineer build?

Data Engineers may develop batch ETL or ELT pipelines, streaming workflows, API integrations, database transfers, and file-based ingestion processes. Pipelines are designed to move data reliably from source systems into warehouses, lakehouses, or downstream applications.

3. Which programming languages are commonly used in data engineering?

Python and SQL are widely used for pipeline development, data transformation, automation, querying, and validation. Depending on the environment, engineers may also work with Java, Scala, or other programming languages for distributed data processing.

4. What data platforms and tools are commonly used?

Common technologies include cloud platforms such as AWS, Azure, and Google Cloud, along with data warehouses, lakehouses, orchestration tools, and distributed processing frameworks. Tools such as Snowflake, Databricks, Apache Spark, Airflow, Kafka, and dbt may also be part of a data engineering stack.

5. How does a Data Engineer ensure data quality?

Engineers use validation checks, automated tests, schema controls, monitoring, logging, and reconciliation processes to identify data issues. These practices help ensure that downstream datasets are accurate, consistent, complete, and available when needed.

6. How does data modeling fit into a Data Engineer’s responsibilities?

Data modeling organizes information into structures that support analytics, reporting, and application needs. Engineers may design tables, relationships, schemas, partitions, and other structures that improve data usability and query performance.

7. How are data pipelines optimized for performance?

Performance can be improved through query optimization, efficient data models, partitioning, parallel processing, caching, and appropriate resource allocation. Engineers monitor workloads and identify bottlenecks to keep pipelines scalable as data volumes increase.

8. What challenges are common in data engineering?

Common challenges include integrating inconsistent source systems, managing schema changes, troubleshooting failed pipelines, processing large datasets, maintaining data quality, and controlling infrastructure costs. Engineers also need to adapt pipelines as business and analytical requirements change.