Senior Data Engineer/Analyst

Senior Data Engineer/Analyst - 3 Year Contract
Qualifications & Experience:
Must-Have:

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

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

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

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

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

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

Nice-to-Have:

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Experience with AI/ML model deployment for predictive analytics.

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

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

Responsibilities:

Data Engineering & Architecture:

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

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

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

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

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

Data Analysis & Business Intelligence:

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

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

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

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

Cloud & Big Data Technologies:

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

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

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

Collaboration & Leadership:

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

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

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

Originally posted on Himalayas