Sr Analytics Data Engineer-AWS/Python/PowerBI
Are you a highly skilled Data Engineer with strong expertise in analytics, cloud technologies, and modern data engineering frameworks? This role offers an exciting opportunity to design advanced data pipelines, build scalable architectures, and deliver meaningful insights that drive strategic business decisions.
Working with one of our top clients, this position calls for a Sr Analytics Data Engineer-AWS/Python/PowerBI who will leverage AWS, Python, SQL, and Power BI to develop end-to-end data solutions. The ideal candidate will bring deep experience in data modeling, ETL engineering, statistical analysis, and large-scale cloud data environments, with the ability to translate complex data into impactful business intelligence.
Responsibilities:
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Collect, preprocess, and cleanse data from multiple sources to ensure accuracy and high data quality.
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Apply statistical modeling techniques—such as regression, clustering, and hypothesis testing—to identify key patterns and relationships.
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Use SQL, Python, or R to analyze large datasets and uncover trends that address critical business challenges.
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Interpret analytical findings and translate them into actionable insights that enhance operational efficiency and customer experience.
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Build compelling visualizations and dashboards using Power BI, Tableau, or matplotlib to support data-driven decision-making.
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Develop predictive models and incorporate machine learning techniques to solve complex business problems.
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Bridge the gap between technical data processes and business needs by collaborating with stakeholders across the organization.
Desired Skill Set:
Programming & Tools:
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Strong proficiency in Python, SAS, and SQL.
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Hands-on experience with Power BI, including DAX and M Code.
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Proficient in MS 365 Suite: Office, Power Automate, SharePoint, and OneDrive.
Database & Data Engineering:
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Advanced SQL Server configuration for high-throughput analytical workloads.
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Experience designing partitioned tables, indexed views, and columnstore indexes.
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Strong understanding of SQL Server recovery models and disaster recovery planning.
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Expertise in building ETL pipelines for large datasets, including IBM Netezza and Hadoop sources.
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Skilled in data staging, incremental loading, and change data capture (CDC).
Data Architecture:
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Experience with star/snowflake schemas, fact/dimension modeling, and slowly changing dimensions (SCD).
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Ability to design batch processing pipelines using Python and SQL.
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Strong understanding of RDBMS, NoSQL, and file formats such as CSV, Parquet, and JSON.
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Ability to translate business needs into scalable, production-ready data models.
Cloud & Big Data:
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Strong experience with AWS services such as Redshift, Glue, and MLOps frameworks.
Nice-to-Have:
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Previous Data Analyst experience.
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