Tredence Inc. is hiring for a fresher entry level Associate Data Engineer role in India

Position:

• Associate Data Engineer

Company:

• Tredence Inc.

Location:

• Bangalore Urban, Karnataka, India

Job type:

• Full-time

Job mode:

• Onsite

Job requisition id:

• Not provided

Years of experience:

• 0-3 years

Company description:

• Tredence Inc. is a global data science and analytics company that focuses on bridging the gap between creating insights and generating business value from them

• With its headquarters in San Jose, Tredence has built a reputation for turning data into actionable strategies and outcomes for its clients

• The company follows a vertical-focused approach and works with several industries, including retail, consumer packaged goods, technology, telecommunications, healthcare, travel, and industrials

• Tredence is known for its strong culture that values curiosity, continuous learning, and accountability

• It has achieved certifications and recognition as a Great Place to Work and a leader in customer analytics services

• Tredence has a diverse team spread across major global hubs, including San Jose, Foster City, Chicago, London, Toronto, Delhi, Chennai, and Bangalore

• It recently expanded its capabilities by acquiring Further Advisory, strengthening its banking and financial services solutions

• Tredence partners with some of the largest retailers and CPG companies worldwide, leveraging its advanced AI-driven decision intelligence platform

• The company’s focus is on delivering data-driven decisions that accelerate client growth and streamline their digital transformations

• As a key player in data science, Tredence continually invests in engineering, analytics, and quality assurance to meet evolving industry demands

Profile overview:

• The Associate Data Engineer role at Tredence is designed for individuals eager to work with large datasets and cutting-edge cloud-based data platforms

• The position requires expertise in data warehousing, data modeling, and implementing scalable data pipelines

• Successful candidates will need to manage ETL and ELT processes and work on data transformation and analysis using PySpark and SQL

• The job involves building data solutions within platforms like Azure Databricks, Snowflake, and Google Cloud Platform, among others

• Collaboration is a significant part of the role, as the Associate Data Engineer will work closely with cross-functional teams to understand client requirements and deliver high-quality data solutions

• The job calls for strong technical skills and the ability to communicate effectively with both technical and non-technical stakeholders

• Candidates should be comfortable working in a consulting environment and adapting to dynamic project requirements

• The role also demands ensuring data quality, integrity, and consistency throughout the lifecycle of data projects

• The company values individuals who can think creatively, learn quickly, and contribute to innovative data solutions

• Working at Tredence means being part of a fast-growing team that prioritizes excellence and data-driven decision-making

Qualifications:

• A solid understanding of SQL, including the ability to write advanced queries, joins, window functions, and aggregations

• Familiarity with database management concepts, such as normalization, entity-relationship models, indexing, and table partitioning

• Proficiency in Python or PySpark for data processing tasks and script automation

• A clear grasp of data warehousing principles, including the differences between OLTP and OLAP systems, and how data modeling is structured in each case

• Direct experience in designing ETL and ELT processes, managing incremental loads, and maintaining historical data accuracy

• Practical experience with data platforms like Databricks or Azure Databricks, Snowflake, Redshift, BigQuery, or similar cloud-based environments

• Ability to work with orchestration tools like Airflow, Azure Data Factory, or dbt to automate data pipeline workflows

• Comfort in managing data models, including star and snowflake schemas, and in handling facts and dimension tables

• Experience in dealing with change data capture and slowly changing dimensions for dynamic data environments

• Clear and concise communication skills, enabling effective collaboration within cross-functional teams and with external stakeholders

Additional info:

• Tredence is experiencing significant growth, with notable increases in engineering, administrative, and legal functions, as well as a doubling of its quality assurance team

• The company’s current workforce is over 3,000 employees, and it is seeing strong momentum in building out its technical capabilities

• The position will be part of a highly collaborative environment that values teamwork and open communication

• The role provides the opportunity to work on client-facing projects, where you can directly contribute to delivering tangible outcomes for customers

• Tredence’s work in retail and CPG sectors is extensive, with its data models powering more than $2 trillion in annual retail and CPG sales

• The Associate Data Engineer will be joining a team that is committed to solving real-world business challenges through advanced data solutions

• Tredence also promotes a strong culture of continuous improvement, with opportunities for growth and upskilling through internal learning initiatives

• The company’s global reach means working alongside a diverse team that brings together perspectives from different regions and industries

• You will have the chance to work on projects that have a direct impact on client operations and decision-making processes

• Tredence is looking for candidates who are not only technically proficient but also proactive and willing to take ownership of projects from start to finish

Key Responsibilities:

• Design and build efficient data pipelines that are scalable and maintainable for large data processing workloads

• Optimize data transformation processes by leveraging PySpark, especially within Databricks and Azure Databricks environments

• Write SQL queries that can handle data transformation, data quality checks, and analytical tasks, with a focus on accuracy and performance

• Develop ETL and ELT workflows that accommodate both full and incremental data loads, ensuring reliable data delivery to downstream systems

• Implement data models that support analytics and reporting, using concepts such as star and snowflake schemas and properly structuring fact and dimension tables

• Work with data concepts like change data capture and slowly changing dimensions to ensure data is current and relevant for business decision-making

• Engage with other team members and stakeholders to identify data requirements and offer technical solutions that meet or exceed project expectations

• Maintain and monitor data quality, data consistency, and data integrity across systems to uphold the standards of Tredence’s data services

• Communicate technical issues and progress updates in an accessible manner to team members who may not have a technical background

• Adapt quickly to changing project requirements and collaborate seamlessly within a fast-paced, client-focused environment

Technical Skills:

• SQL expertise, particularly with complex joins, subqueries, window functions, and aggregate operations for robust data handling

• A strong grasp of database structures, including primary and foreign key relationships, indexes for performance, and data normalization techniques

• Programming experience in Python or PySpark to support data manipulation and pipeline creation

• Familiarity with cloud-based data warehouse tools and technologies, especially Databricks and Azure Databricks, for building data solutions

• Hands-on experience in data transformation and ETL/ELT pipeline construction, leveraging cloud orchestration and automation tools for efficiency

• Knowledge of data modeling strategies, from normalized relational models to analytic-focused star and snowflake schemas

• Ability to manage incremental data loads to ensure systems have the latest data while preserving historical records for analysis

• Exposure to best practices for managing data change over time, particularly using change data capture and slowly changing dimensions

• Experience with data orchestration frameworks, which ensure data flows smoothly and reliably between systems

• Understanding of how data quality impacts business outcomes and a commitment to delivering trustworthy data

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