Data Engineer

Job Description

Data Engineer – AWS & Databricks

\nRole Overview

\nThe Client Data Engineer will be responsible for the design, development, validation, optimization, and ongoing ownership of data solutions primarily using AWS and Databricks . The role will work closely with internal teams, business SMEs, architects, governance teams, Power BI/analytics teams, and implementation partners.

\nThe ideal candidate should have 8+ years of experience in data engineering, ETL/ELT, and data integration , with strong hands-on expertise in Databricks, AWS, SQL, Python, PySpark, and Spark .

\nKey Responsibilities

\n\n • Design, develop, and support AWS and Databricks data pipelines using SQL, Python, PySpark, and Spark.

\n • Develop and maintain Bronze, Silver, and Gold data layers using Databricks and Delta Lake.

\n • Work with source-system data, business rules, mappings, and integration requirements.

\n • Review and validate data solutions delivered by implementation partners.

\n • Perform data profiling, reconciliation, testing, and troubleshooting.

\n • Support data quality, metadata, lineage, and Unity Catalog activities.

\n • Optimize Databricks pipelines and queries for performance, scalability, and cost efficiency.

\n • Participate in code reviews, CI/CD, deployment, and production support.

\n • Maintain technical documentation and support knowledge transfer to internal teams.

\n • Collaborate closely with BI and analytics teams on downstream reporting and data requirements.

\n • Support integration with Tableau and Power BI for reporting and analytics use cases.

\n\nRequired Skills

\n\n • 8+ years of experience in data engineering, ETL/ELT, or data integration.

\n • Strong hands-on experience with Databricks .

\n • Strong experience with AWS data services and cloud-based data platforms .

\n • Strong SQL and Python skills.

\n • Strong experience with PySpark / Apache Spark .

\n • Experience designing and implementing data pipelines and data transformation frameworks.

\n • Strong understanding of data integration, transformation, reconciliation, and data quality.

\n • Strong analytical, troubleshooting, and communication skills.

\n\nPreferred Skills

\n\n • Databricks, Delta Lake, and Unity Catalog .

\n • AWS Databricks / Databricks on AWS experience.

\n • Medallion/Lakehouse architecture.

\n • Experience with Tableau reporting and Power BI .

\n • Git, CI/CD, and DevOps practices.

\n • Experience with data governance, metadata, lineage, and data quality frameworks.

\n • Databricks certification is a plus.

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