Senior Software Engineer

Compensation: Depends on Experience (DOE)

• Collaborate with Stakeholders – Work closely with business leaders, product managers, and analysts to understand requirements and translate them into technical solutions.
• Design Scalable Solutions – Architect and develop robust software solutions that address complex business problems and ensure seamless data integration across multiple applications.
• Data Analysis & Insights – Analyze data from various sources to identify trends, patterns, and actionable insights that drive business decisions.
• Develop & Optimize Data Pipelines – Design, build, and maintain efficient ETL processes and data pipelines to support analytical and reporting needs.
• Visualization & Reporting – Create intuitive dashboards and presentations that effectively communicate data-driven insights to both technical and non-technical audiences.
• Cross-functional Collaboration – Coordinate with engineering, analytics, and business teams to align on data strategies and ensure data accuracy and consistency.
• Client & Stakeholder Engagement – Facilitate client discussions, present findings, and coordinate feedback loops to refine solutions based on business needs.
• Technical Leadership – Provide guidance on best practices, mentor junior engineers, and drive innovation in data engineering and analytics.

• BI & Visualization Tools – Proficiency in Tableau, Power BI, or similar tools for data visualization and reporting.
• Database Expertise – Strong experience with SQL and No
  
  SQL databases for querying, data modeling, and performance optimization.
• Cloud Data Platforms – Hands-on experience with Azure Data Lake, AWS Redshift, Google Big Query, or other cloud-based data solutions.
• ETL & Data Engineering – Expertise in Azure Data Factory (ADF), Apache Spark, Airflow, or other ETL tools to design scalable data pipelines.
• Programming for Data Processing – Proficiency in Python (Pandas, Num Py, PySpark), R, or similar for data manipulation and analysis.
• Big Data & Streaming – Familiarity with Kafka, Snowflake, Hadoop, or Databricks for handling large-scale data processing.
• Problem-Solving & Business Acumen – Ability to translate complex data into actionable insights and align solutions with business goals.
• Collaboration & Communication – Strong interpersonal skills with a positive, “can-do” attitude to work effectively with cross-functional teams.