Senior Data & Analytics Engineer

Job Description: Responsibilities: - Senior Data & Analytics Engineer
A Senior Data & Analytics Engineer is a pivotal role that focuses on designing, building, and maintaining an organization's data infrastructure and pipelines. Here's a breakdown of the core responsibilities:Data Architecture & Design: Design and implement scalable, reliable, and efficient data architectures and pipelines using various tools and technologies.ETL/ELT Development: Develop and optimize ETL (Extract, Transform, Load) and ELT pipelines to integrate data from disparate sources into a centralized data warehouse or lake.Data Modeling: Create and manage data models (e.g., star schema, snowflake schema) that support analytical and reporting needs.Performance Optimization: Tune database queries and data pipelines to improve performance, reduce latency, and ensure efficient resource utilization.Data Governance & Quality: Establish and enforce best practices for data quality, security, and governance. This includes implementing data validation rules and monitoring data lineage.Collaboration & Mentorship: Work closely with data scientists, business analysts, and other engineers to understand data needs and deliver solutions. Mentor junior team members and foster a culture of technical excellence.Technology Evaluation: Research and evaluate new data technologies and tools to improve the existing data platform.Required Qualifications & SkillsThis role requires a blend of technical expertise, analytical thinking, and leadership.Experience: 8 to 12 years of hands-on experience in data engineering, business intelligence, or a similar role. A proven track record of designing and implementing large-scale data solutions is a must.Technical Skills:Programming Languages: Expert proficiency in Python or Scala is essential for scripting and automation.SQL: Advanced knowledge of SQL for complex data manipulation, query optimization, and data modeling.Cloud Platforms: Extensive experience with at least one major cloud platform (e.g., AWS, GCP, or Azure) and its data services (e.g., AWS S3, Redshift, Glue; GCP BigQuery, Dataflow; Azure Data Lake, Synapse).Big Data Technologies: Hands-on experience with big data technologies such as Apache Spark, Hadoop, or Databricks.Data Warehousing: Deep understanding of data warehousing concepts and experience with data warehouses like Snowflake, Redshift, or BigQuery.Orchestration Tools: Familiarity with data orchestration tools like Apache Airflow, Dagster, or Prefect.Database Management: Solid understanding of both relational and NoSQL databases.Soft Skills:Problem-Solving: Strong analytical and problem-solving skills with the ability to translate business requirements into technical solutions.Communication: Excellent communication skills to effectively collaborate with technical and non-technical stakeholders.Leadership: Ability to take ownership of projects, work independently, and mentor others.Desired Certifications (Bonus)Cloud-specific certifications (e.g., AWS Certified Data Analytics - Specialty, Google Cloud Professional Data Engineer).Certifications related to specific tools (e.g., Databricks Certified Associate Developer for Apache Spark).