The data Analytics Engineer rtole will be an integral part of client projects to design, develop and deliver decisioning intelligence solutions. Being part of this team will entail working in a collaborative manner with other team members, and across multiple business and technical entities on the client side.
As a key member of a modern data team, the primary responsibility for this role would be to bridge the gap between the enterprise data engineers and business focused data and visualization analysts. You will be responsible for transforming raw data into clean, organized, and reusable datasets that enable effective analysis and decisioning intelligence data products. Below is an overview of their role, responsibilities, and required skills:
Key Responsibilities:
Design, develop, and maintain clean, tested, and scalable data models to support analytics and business intelligence needs. These data models will be created to serve business analysis objectives, and the role's responsibility will entail defining rules and requirements for the data.
Collaborate with data analysts and business stakeholders to define data requirements and ensure data consistency across different platforms, and to promote self-service analytics.
Build, optimize, and document transformed pipelines into visualization and analysis environments to ensure high data quality and integrity.
Implement data transformation best practices using modern tools such as dbt (Data Build Tool), SQL, and cloud data warehouses (e.g., Azure Synapse, BigQuery, Azure Databricks, etc.). These transformations may include:
Identifying and removing inaccurate or corrupted data, along with identifying data gaps that will impact analysis efficacy.
Carrying out data formatting and processing tasks to build foundation layers for analysis and visualization activities.
Joining multiple database tables by their matching attributions to create foundation tables for analysis purposes
Monitor and troubleshoot data quality issues, ensuring data accuracy, completeness, and reliability.
Define and maintain data quality metrics to guarantee accuracy for analytics needs.
Maintaining data quality with alignment on data formats used in all analysis use-cases.
Adopt automated methods to cleanse and improv data quality.
Optimize data performance ensuring query efficiency for large datasets.
Establish and maintainanalytics platform best practices for the team:
Version control to track history of changes in datasets and rolling back to older version of something goes wrong.
Data unit testingto examine chunks of data transformation for quality and correspondence to defined analytics tasks.
Continuous integration and delivery (CI/CD)to ensure up-to-date and reliable data.
Maintain documentation to ensure that everyone on the team uses the same data definitions and language.
This role requires a high degree of collaboration with other team members, incl. data engineers, business and visualization analysts, and data scientists to align data assets to business analysis objectives.
Work closely with data engineering teams to enable new data sources into the data-lake and optimize performance.
Act as consultant within cross-functional teams to understand business needs and develop appropriate data solutions.
Communication skills, both written and verbal, should be professional, concise, and effective.
Ability to “own” your work – taking initiative, being proactive, anticipating and completing projects in a comprehensive manner.
Exhibiting a willingness to continuously learn, problem-solve, and help others.
What We would Love to See:
Technical Proficiency: Strong knowledge of SQL and Python.
Familiarity with cloud platforms like Azure, Azure Databricks, and Google BigQuery.
Understanding of schema design and data modeling methodologies.
Hands-on experience with dbt (Data Build Tool) for data transformation and modeling.
Software Engineering Practices: Experience with version control systems (e.g., Git) and CI/CD workflows.
A passion for continuous improvement and learning – for example, keeping up to datewith Artificial Intelligence technologies, tools, and methodsand applying them to your everyday activities.
Communication Skills: Ability to translate technical concepts for non-technical stakeholders.
Problem-Solving: Analytical mindset to address business challenges through data design.
Bachelor's or master's degree in computer science, Data Science, Engineering, or a related field.
Strong problem-solving skills and attention to detail.
Why Join Us?
Opportunity to work with cutting-edge business and data challenges. technologies.
Collaborative and innovative work environment.
Competitive salary and benefits package.
Career growth opportunities in a data-driven organization.
If you are passionate about data and enjoy creating efficient, scalable data solutions, we would love to hear from you!Benefits and Perks at Rightpoint
30 Paid leaves
Public Holidays
Casual and open office environment
Flexible Work Schedule
Family medical insurance
Life insurance
Accidental Insurance
Regular Cultural & Social Events including Diwali Party, Team Parties, Team outings, etc.
Continuous Training, Certifications, and Learning Opportunities First-hand experience dealing with security incidents.
EEO Statement Rightpoint, a Genpact Company, is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color, religion or belief, sex, age, national origin, citizenship status, marital status, military/veteran status, genetic information, sexual orientation, gender identity, physical or mental disability or any other characteristic protected by applicable laws. We are committed to creating a dynamic work environment that values diversity and inclusion, respect and integrity, customer focus, and innovation.
