Senior Data Engineer​/Analyst

Qualifications & Experience:

Must-Have:

Bachelors or Masters degree in Computer Science, Data Science, Engineering, Mathematics, or a related field.

5+ years of experience in data engineering, analytics, or BI development.

Strong proficiency in SQL and Python for data manipulation and transformation.

Experience with ETL/ELT processes, data modeling, and data warehousing concepts.

Expertise in cloud platforms (AWS, Azure, or GCP) and big data tools (Spark, Snowflake, Databricks, Kafka).

Familiarity with data visualization tools (Power BI, Tableau, Looker).

Nice-to-Have:

Experience with AI/ML model deployment for predictive analytics.

Knowledge of Dev Ops for data (CI/CD, Infrastructure-as-Code).

Certifications in AWS Data Analytics, Azure Data Engineer, or Google Cloud Professional Data Engineer.

Responsibilities:

Data Engineering & Architecture:

Design, develop, and maintain scalable and efficient ETL pipelines for data ingestion, transformation, and storage.

Build and optimize data warehouses, data lakes, and real-time streaming solutions to support business intelligence and analytics needs.

Ensure data quality, integrity, and security across all data processing workflows.

Collaborate with Data Scientists, Analysts, and Software Engineers to design data models that enable advanced analytics.

Implement data governance, cataloging, and lineage tracking to ensure transparency and compliance.

Conduct data exploration, statistical analysis, and trend identification to extract actionable insights.

Develop interactive dashboards and reports using BI tools like Power BI, Tableau, or Looker.

Work closely with business teams to understand KPIs and performance metrics, translating data into valuable insights.

Optimize query performance and database efficiency for large-scale data processing.

Design and manage cloud-based data solutions (AWS, Azure, GCP) with services such as AWS Glue, Azure Data Factory, Google Big Query, Snowflake, and Databricks.

Work with big data frameworks like Apache Spark, Hadoop, or Kafka for distributed data processing.

Develop automated data pipelines using orchestration tools like Airflow, Prefect, or Luigi.

Work cross-functionally with engineering, product, and business teams to define data requirements.

Mentor junior team members and provide guidance on best practices in data engineering and analytics.

Drive continuous improvement initiatives in data architecture, automation, and AI-driven analytics.