EE
Data Engineer
- Hiring from
- United States
- Work type
- Hybrid
- Posted
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12 Months ( Hybrid role 3 days a week)
Location: St. Louis, MO
Non-local candidate is fine but 100% open to relocate
Key Responsibilities
Data Engineering & Development
" Design, develop, test, and deploy high-quality, secure, scalable, and resilient data pipelines using Apache Spark, Java/Scala across Hadoop and cloud-native object storage platforms.
" Build and maintain batch and near real-time data processing frameworks capable of supporting petabyte-scale workloads.
" Develop reusable engineering components and frameworks that accelerate data product delivery while maintaining enterprise standards.
Architecture & Platform Engineering
" Design and implement a "build once, run anywhere " architecture supporting seamless deployment across on-premises and public cloud environments without code changes.
" Implement data lineage, metadata management, data cataloguing, data quality controls, and observability capabilities across the data ecosystem.
" Collaborate with architects and platform teams to establish scalable design patterns and engineering best practices.
Cloud Modernization
" Contribute to migration initiatives moving legacy ETL and data warehouse workloads from on-premises environments to cloud-native architectures.
" Leverage cloud services such as Amazon S3, EMR, Glue, and related data services to improve scalability, reliability, and operational efficiency.
" Drive adoption of modern Lakehouse and distributed compute architectures.
Delivery & Technical Leadership
" Lead end-to-end development activities including requirement analysis, solution design, coding, testing, deployment, and production support.
" Mentor and guide junior engineers through code reviews, technical coaching, and engineering best practices.
" Partner with product owners, analysts, architects, and business stakeholders to deliver high-quality solutions within committed timelines.
Operational Excellence
" Troubleshoot complex production incidents and perform root cause analysis to identify and implement long-term remediation strategies.
" Ensure compliance with Client's engineering, security, quality assurance, and operational governance standards.
" Continuously identify opportunities to improve performance, automation, monitoring, and process efficiency.
Innovation
" Evaluate emerging data technologies and conduct proof-of-concept (POC) initiatives to determine their applicability within Client's data ecosystem.
" Contribute to engineering innovation and continuous improvement initiatives across the organisation.
Qualifications
Required Experience
" 8-10 yrs. experience years of experience delivering enterprise-scale Data Warehouse, Data Lake, or Data Lakehouse solutions.
" Proven experience implementing multiple end-to-end data engineering projects within large-scale distributed computing environments.
" Hands-on experience migrating ETL and analytics workloads from on-premises platforms to cloud-native environments.
Technical Expertise
" Strong development experience using: Apache Spark, Scala or Java, Hadoop ecosystem technologies, Object Storage platforms
" Experience building orchestration and workflow solutions using: Apache Airflow/ Apache NiFi and Similar enterprise scheduling frameworks
" Strong SQL expertise and experience with Relational and NoSQL database technologies: Oracle/ SQL Server, Cassandra, Dynamo DB etc.
" Working knowledge of cloud platforms, preferably AWS: Amazon S3, EMR, AWS Glue, Cloud-native data services
Professional Skills
" Strong analytical and problem-solving capabilities.
" Experience operating within Agile delivery environments.
" Excellent written and verbal communication skills.
" Proven ability to collaborate within geographically distributed and matrix-based teams.
" Self-starter with strong ownership, accountability, and execution focus.
" Ability to learn emerging technologies quickly and apply them effectively to business challenges.
Location: St. Louis, MO
Non-local candidate is fine but 100% open to relocate
Key Responsibilities
Data Engineering & Development
" Design, develop, test, and deploy high-quality, secure, scalable, and resilient data pipelines using Apache Spark, Java/Scala across Hadoop and cloud-native object storage platforms.
" Build and maintain batch and near real-time data processing frameworks capable of supporting petabyte-scale workloads.
" Develop reusable engineering components and frameworks that accelerate data product delivery while maintaining enterprise standards.
Architecture & Platform Engineering
" Design and implement a "build once, run anywhere " architecture supporting seamless deployment across on-premises and public cloud environments without code changes.
" Implement data lineage, metadata management, data cataloguing, data quality controls, and observability capabilities across the data ecosystem.
" Collaborate with architects and platform teams to establish scalable design patterns and engineering best practices.
Cloud Modernization
" Contribute to migration initiatives moving legacy ETL and data warehouse workloads from on-premises environments to cloud-native architectures.
" Leverage cloud services such as Amazon S3, EMR, Glue, and related data services to improve scalability, reliability, and operational efficiency.
" Drive adoption of modern Lakehouse and distributed compute architectures.
Delivery & Technical Leadership
" Lead end-to-end development activities including requirement analysis, solution design, coding, testing, deployment, and production support.
" Mentor and guide junior engineers through code reviews, technical coaching, and engineering best practices.
" Partner with product owners, analysts, architects, and business stakeholders to deliver high-quality solutions within committed timelines.
Operational Excellence
" Troubleshoot complex production incidents and perform root cause analysis to identify and implement long-term remediation strategies.
" Ensure compliance with Client's engineering, security, quality assurance, and operational governance standards.
" Continuously identify opportunities to improve performance, automation, monitoring, and process efficiency.
Innovation
" Evaluate emerging data technologies and conduct proof-of-concept (POC) initiatives to determine their applicability within Client's data ecosystem.
" Contribute to engineering innovation and continuous improvement initiatives across the organisation.
Qualifications
Required Experience
" 8-10 yrs. experience years of experience delivering enterprise-scale Data Warehouse, Data Lake, or Data Lakehouse solutions.
" Proven experience implementing multiple end-to-end data engineering projects within large-scale distributed computing environments.
" Hands-on experience migrating ETL and analytics workloads from on-premises platforms to cloud-native environments.
Technical Expertise
" Strong development experience using: Apache Spark, Scala or Java, Hadoop ecosystem technologies, Object Storage platforms
" Experience building orchestration and workflow solutions using: Apache Airflow/ Apache NiFi and Similar enterprise scheduling frameworks
" Strong SQL expertise and experience with Relational and NoSQL database technologies: Oracle/ SQL Server, Cassandra, Dynamo DB etc.
" Working knowledge of cloud platforms, preferably AWS: Amazon S3, EMR, AWS Glue, Cloud-native data services
Professional Skills
" Strong analytical and problem-solving capabilities.
" Experience operating within Agile delivery environments.
" Excellent written and verbal communication skills.
" Proven ability to collaborate within geographically distributed and matrix-based teams.
" Self-starter with strong ownership, accountability, and execution focus.
" Ability to learn emerging technologies quickly and apply them effectively to business challenges.