Allata is a global consulting and technology services firm with offices in the US, India, and Argentina. We help organizations accelerate growth, drive innovation, and solve complex challenges by combining strategy, design, and advanced technology. Our expertise covers defining business vision, optimizing processes, and creating engaging digital experiences. We architect and modernize secure, scalable solutions using cloud platforms and top engineering practices.
Allata also empowers clients to unlock data value through analytics and visualization and leverages artificial intelligence to automate processes and enhance decision-making. Our agile, cross-functional teams work closely with clients, either integrating with their teams or providing independent guidance—to deliver measurable results and build lasting partnerships.
If you are a smart & passionate team player - then this Junior Data Engineer opportunity is for you!
We at IMRIEL (An Allata Company) is looking for enthusiastic and motivated Junior Data Engineers who possess a solid foundation in computer science, data engineering principles, cloud data platforms, and analytics concepts. In this role, you will work closely with experienced engineers and analytics teams to understand, design, and support data pipelines and analytical solutions that enable data-driven decision-making.
What you'll be doing:
• Understanding and supporting the design of scalable data pipelines for ingesting, transforming, and delivering data from multiple sources.
• Assisting in the conceptual design and implementation of ETL / ELT workflows using platforms such as Snowflake, Azure, and AWS.
• Developing and analyzing SQL queries for data extraction, transformation, aggregation, and validation.
• Supporting data modeling activities by understanding fact tables, dimension tables, and analytical schemas.
• Gaining exposure to cloud-based data warehouses and data lakes, and understanding how data flows across systems.
• Assisting in testing, validation, and troubleshooting of data pipelines to ensure data accuracy and consistency.
• Maintaining documentation for data pipelines, transformations, and analytical models.
What you need:
Basic Skills:
• Strong conceptual understanding of Object-Oriented Programming (OOPs) concepts such as classes, objects, inheritance, polymorphism, encapsulation, and abstraction.
• Solid knowledge of Data Structures and Algorithms, including arrays, lists, stacks, queues, hash maps, and trees.
• Foundational knowledge of Python programming, particularly for data manipulation and scripting.
• Good understanding of SQL, including joins, subqueries, CTEs, aggregations, and window functions.
• Clear understanding of Data Engineering fundamentals, including data ingestion, transformation, storage, and consumption layers.
• Understanding of data pipelines and how data moves from source systems to analytics platforms.
• Conceptual knowledge of ETL and ELT approaches and their differences.
• Awareness of Snowflake concepts, including databases, schemas, warehouses, and stages.
• Basic understanding of cloud data services on Azure and AWS, such as Azure Data Factory, Azure Data Lake, Blob Storage, and AWS services including S3, AWS Glue, and Redshift Etc.
Responsibilities:
• Assist in understanding, developing, and supporting data pipelines for structured and semi-structured data.
• Support the creation and execution of ETL / ELT workflows for data integration and transformation.
• Collaborate with team members to understand cloud-based Data Warehouses and Data Lakes.
• Perform SQL-based data extraction, transformation, and validation as per analytical requirements.
• Support data quality checks and validation activities to ensure reliable data outputs.
• Document data workflows, pipeline logic, data models, and BI metrics for knowledge sharing.
Good To Have:
• Academic or project-based exposure to data engineering tools or cloud platforms.
• Awareness of distributed data processing concepts such as Apache Spark or Hadoop.
• Conceptual understanding of Business Intelligence tools such as Power BI.
• Knowledge of DAX concepts, including measures, calculated columns, and basic calculations.
• Understanding of BI fundamentals, including facts, dimensions, metrics, KPIs, and dimensional modeling.
• Relevant coursework, certifications, or training in Data Engineering, Cloud, Python, SQL, or BI tools.
Personal Attributes:
• Innovative thinker with a proactive approach to overcoming challenges.
• Strong analytical and problem-solving skills.
• Willingness to learn and adapt to new tools, technologies, and data platforms.
• Good communication and collaboration skills.
• Attention to detail and a strong conceptual mindset.
• Passion for data, analytics, and engineering fundamentals