Senior Data Analytics Engineer

Job Description

Job Description - We are seeking a Senior Data Analytics Engineer who can take ownership of analytics ecosystems end-to-end ( full-stack) from raw data ingestion to business-ready dashboards. The ideal candidate has a blend of data engineering skills and an eye for storytelling through data. You"ll work with Python , Databricks , ADF , Snowflake , MS Fabric and SQL to build robust data pipelines, and use Power BI, DAX, and Power Query to deliver actionable insights. You will play a critical role in designing, developing, and maintaining end-to-end data solutions, from building robust data pipelines and models to delivering insightful dashboards and visualizations that empower informed decision-making across the organization and for our clients. Key Responsibilities: Design, build, and maintain robust data pipelines and data models using tools such as Python, Databricks, Azure Data Factory, Snowflake, MS Fabric and MS SQL to ensure accurate, reliable, and scalable data flows from source to dashboard. Work may also involve other modern data platforms and cloud technologies as needed. Create dynamic, interactive Power BI dashboards and reports tailored to meet business needs. Ensure visualizations are user-friendly, visually compelling, and deliver meaningful insights for decision-makers. Ingest, transform, and model large datasets efficiently. Optimize ETL/ELT workflows, SQL queries, and Power BI data models to deliver high-performance analytics solutions. Maintain comprehensive documentation for data pipelines, models, dashboards, and processes to support transparency, reproducibility, and cross-team collaboration. Experience: 5+ years of hands-on experience working across the data lifecycle, including data engineering, modeling, and business intelligence development, whether in a single full-stack role or through multiple complementary positions. The ability to showcase previous work through a GitHub repository, portfolio of reports/dashboards, or other demonstrable project examples is highly valued. Technical Proficiency: Proven experience designing, developing, and maintaining end-to-end data solutions across both data engineering and business intelligence domains. Strong proficiency in Python for data processing, automation, and integration. Hands-on experience with Azure Data Factory (ADF), Databricks, and Microsoft Fabric for building and orchestrating ETL/ELT pipelines. Solid understanding of data warehousing and data modeling principles (e.g., star/snowflake schemas, dimensional modeling). Proficiency in SQL and experience with MS SQL Server, Snowflake, PostgreSQL, and Oracle / PL-SQL. Exposure to NoSQL databases such as MongoDB and big data ecosystems (e.g., Spark, Delta Lake, Azure Data Lake, Hadoop, or similar distributed data technologies). Familiarity with data integration tools and API-based data ingestion is a plus. Business Intelligence & Visualization: Advanced expertise in Power BI, including Power Query (M language), DAX, and data model design. Ability to create interactive, visually compelling, and performance-optimized dashboards for business stakeholders. Understanding data storytelling, KPI design, and user experience principles in dashboarding. CI/CD: Experience implementing CI/CD pipelines for data workflows and BI assets using Azure DevOps, Git, or similar tools. Familiarity with source control management for Power BI datasets, notebooks, and data pipeline code. Ability to manage deployment workflows in Microsoft Fabric. Understanding branching strategies, code review processes for data and analytics projects. Commitment to maintaining version-controlled, auditable, and reusable analytics solutions across the data stack. Data Science (Nice To Have): Experience with data science and statistical analysis, including exposure to machine learning models, predictive analytics, or forecasting techniques. (Nice to Familiarity with R or Python libraries such as scikit-learn, pandas, or TensorFlow for data exploration and model development