The Role
Design, build, and maintain scalable streaming and batch data pipelines and data products. Support data infrastructure, modeling, and formats for data lakes/warehouses. Collaborate with analytics stakeholders, implement deployment automation, and prototype data engineering frameworks to enable reliable data access and advanced analytics.
Summary Generated by Built In
Job Description: The Professional, Data Engineering job designs, builds and maintains moderately complex data systems that enable data analysis and reporting. With limited supervision, this job collaborates to ensure that large sets of data are efficiently processed and made accessible for decision making.
ESSENTIAL FUNCTIONS
• DATA & ANALYTICAL SOLUTIONS: Develops moderately complex data products and solutions using advanced data engineering and cloud-based technologies, ensuring they are designed and built to be scalable, sustainable and robust.
• DATA PIPELINES: Maintains and supports the development of streaming and batch data pipelines that facilitate the seamless ingestion of data from various data sources, transform the data into information and move to data stores like data lake, data warehouse and others.
• DATA SYSTEMS: Reviews existing data systems and architectures to implement the identified areas for improvement and optimization.
• DATA INFRASTRUCTURE: Helps prepare data infrastructure to support the efficient storage and retrieval of data.
• DATA FORMATS: Implements appropriate data formats to improve data usability and accessibility across the organization.
• STAKEHOLDER MANAGEMENT: Partners with multi-functional data and advanced analytic teams to collect requirements and ensure that data solutions meet the functional and non-functional needs of various partners.
• DATA FRAMEWORKS: Builds moderately complex prototypes to test new concepts and implements data engineering frameworks and architectures to support the improvement of data processing capabilities and advanced analytics initiatives.
• AUTOMATED DEPLOYMENT PIPELINES: Implements automated deployment pipelines to support improving efficiency of code deployments with fit for purpose governance.
• DATA MODELING: Performs moderately complex data modeling aligned with the datastore technology to ensure sustainable performance and accessibility.
PREFERRED QUALIFICATIONS
• CLOUD ENVIRONMENTS: Familiarity with major cloud platforms (AWS, GCP, Azure).
• DATA ARCHITECTURE: Experience with modern data architectures, including data lakes, data lakehouses, and data hubs, along with related capabilities such as ingestion, governance, modeling, and observability.
• DATA INGESTION: Proficiency in data collection, ingestion tools (Kafka, AWS Glue), and storage formats (Iceberg, Parquet).
• DATA STREAMING: Knowledge of streaming architectures and tools (Kafka, Flink).
• DATA MODELING: Strong background in data transformation and modeling using SQL-based frameworks and orchestration tools (dbt, AWS Glue, Airflow). Experience with modeling concepts like SCD and schema evolution.
• DATA TRANSFORMATION: Familiarity with using Spark for data transformation, including streaming, performance tuning, and debugging with Spark UI.
• PROGRAMMING: Proficient with programming in Python, Java, Scala, or similar languages. Expert-level proficiency in SQL for data manipulation and optimization.
• DEVOPS: Demonstrated experience in DevOps practices, including code management, CI/CD, and deployment strategies.
• DATA GOVERNANCE: Understanding of data governance principles, including data quality, privacy, and security considerations for data product development and consumption.
MINIMUM & TYPICAL YEARS OF WORK EXPERIENCE
• Minimum requirement of 2 years of relevant work experience. Typically reflects 3 years or more of relevant experience.
Skills Required
- Minimum 2 years of relevant work experience
- Proficient programming in Python, Java, Scala, or similar languages
- Expert-level proficiency in SQL for data manipulation and optimization
- Familiarity with major cloud platforms (AWS, GCP, Azure)
- Experience with modern data architectures (data lakes, lakehouses, hubs) and related capabilities
- Proficiency with data ingestion tools and formats (Kafka, AWS Glue, Iceberg, Parquet)
- Knowledge of streaming architectures and tools (Kafka, Flink)
- Experience with data transformation and modeling using SQL-based frameworks and orchestration tools (dbt, AWS Glue, Airflow); SCD and schema evolution
- Familiarity with Spark for data transformation, streaming, performance tuning, and debugging (Spark UI)
- Demonstrated experience in DevOps practices, CI/CD, and deployment strategies
- Understanding of data governance principles, data quality, privacy, and security for data products
Am I A Good Fit?
Get Personalized Job Insights.
Our AI-powered fit analysis compares your resume with a job listing so you know if your skills & experience align.
Success! Refresh the page to see how your skills align with this role.
The Company
What We Do
AlgoLeap specializes in AI-powered software solutions, digital product engineering, and IT consulting services, focusing on digital transformation and AI-driven innovation.
