The Data Engineer will play a key role in building and maintaining a modern data infrastructure. They will design, develop, and optimize ELT/ETL pipelines and implement efficient data models to support analytics, reporting, and business decision-making. The role involves deep collaboration with cross-functional teams to deliver scalable and high-performing data solutions using tools such as Snowflake, ADF, dbt, and Python.
Responsibilities include:

• Design, build, and maintain scalable and robust data pipelines using ELT/ETL patterns to ingest, transform, and integrate data.
• Architect and implement efficient data models using Star, Snowflake, and One Wide Table (OWD) design patterns.
• Maintain and create documentation of data architecture, data pipelines, and processes to ensure transparency and reproducibility.
• Integrate data from multiple sources including databases, APIs, and third-party platforms using tools like Azure Data Factory (ADF) and dbt.
• Lead technical discussions, advocate for best practices, and ensure solid data foundations and high standards in data engineering workflows.
• Optimize data systems for performance and cost efficiency using partitioning, clustering, caching, indexing, and fine-tuning techniques.
• Perform QA audits, manage data loads, generate memo files, and handle ad hoc data requests to ensure data integrity and reliability.
• Support analytics and reporting by developing reusable metrics, dashboards, and self-service tools in Power BI and/or Sisense.
• Enhance SDLC by incorporating CI/CD pipelines, version control (e.g., Git), and continuous improvement practices into data engineering processes.
• Collaborate with internal and external stakeholders to gather requirements and deliver comprehensive data solutions.

Education:

• Bachelor's Degree in Computer Science, Mathematics, Statistics, Finance, Information systems or equivalent related technical field experience

Experience:

• 5+ years of professional experience in data engineering, data analytics, or a similar technical role.
• Strong SQL skills with advanced knowledge of Joins, Unions, CTEs, Aggregations, Lag/Lead, and optimization techniques.
• Proficiency in Python for data manipulation, scripting, and automation.
• Experience working with Snowflake, dbt, and Azure Data Factory (ADF).
• Demonstrated experience in data modeling, including dimensional and modern approaches (Star, Snowflake, OWD).
• Hands-on experience in building and maintaining data pipelines (ETL/ELT).
• Understanding of cost optimization, caching, partitioning, and indexing strategies for performance tuning.
• Familiarity with BI tools such as Power BI, Sisense, Looker, Tableau, and Domo.
• Experience with customer personalization solutions and handling large datasets.
• Exposure to scripting languages like Python, Perl, or Shell.

Tools & Skills:

• Deep understanding of complex SQL and Snowflake SQL syntax, including Time Travel, Streams, Cloning, and Role-Based Access.
• Strong knowledge of Snowflake, Azure Data Factory, and dbt.
• Experience with version control systems and CI/CD workflows.
• Knowledge of DataBricks (ADB preferred) and ability to interpret existing solutions.
• Familiarity with reporting tools, especially Power BI and/or Sisense.
• Advanced proficiency in Python and Excel for data analysis and transformation.
• Understanding of data warehousing, proactive data quality monitoring, and structured/unstructured data formats including JSON.

Key Competencies:

• Proven problem-solving skills and high attention to detail.
• Ability to partner with business stakeholders to define questions and build data sets to answer them.
• Capable of navigating ambiguity and balancing multiple priorities in a fast-paced environment.
• Excellent communication and presentation skills for technical and non-technical audiences.
• Self-starter with a spirit of innovation and consistent delivery.
• Demonstrated ability to work collaboratively in multi-disciplinary teams and produce results quickly.

Assets:

• Experience in Telecom or banking industries, especially related to data collection or retention.
• Hands-on experience with ADF data transformations for custom reporting models.
• Experience in scripting and automation using Python, Perl, or Shell.
• Familiarity with data transformations using tools like dbt.
• Data analysis, report development, and business analysis
• Experience with tools like Looker, Excel, Power BI, Tableau, R, SAS

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