Work Schedule
Other
Environmental Conditions
Office
Job Description
Summarized Purpose:
We are seeking a Lead Database Engineer to design, build, optimize, and support AWS-based data lake, data warehouse, and database platforms. This role will lead database architecture, performance tuning, data quality, lineage, source-to-target mapping, production support, and technical delivery across PostgreSQL, Redshift, Athena, DynamoDB, SQL Server, and related AWS services.
Education/Experience:
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Bachelor's degree or equivalent experience and relevant formal academic/vocational qualification.
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Previous roles showcasing 7+ years of database engineering, data architecture, AWS data platform, SQL development, performance tuning, and production support experience, or an equivalent blend of education, training, and experience.
Major Job Responsibilities:
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Design, build, and maintain AWS-based data lake and warehouse architecture using S3, Redshift, DynamoDB, RDS, Athena, and related cloud data services.
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Optimize PostgreSQL, Redshift, Athena, DynamoDB, SQL Server, and SQL-based workloads for performance, reliability, scalability, and cost.
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Lead data integrations, ETL processes, database operations, and source-to-target mapping based on stakeholder and collaborator requirements.
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Maintain and improve existing integrations while adding new EMR, clinical, operational, and enterprise data integrations.
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Implement data quality frameworks, automated validation, reconciliation, monitoring, and alerting to ensure accurate warehouse and lakehouse loads.
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Normalize EMR, claims, clinical, operational, and flat-file data into data warehouse, analytical, and downstream reporting structures.
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Develop and maintain mapping tables, data dictionaries, source-to-target mappings, metadata, lineage, and technical documentation for analysts and downstream systems.
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Link and transform patient activity, operational, and business data into standardized outputs for analytics and reporting.
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Implement backup, recovery, replication, retention, and operational readiness requirements for production databases and data platforms.
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Maintain database documentation describing data elements, transformations, lineage, interfaces, ownership, and usage patterns.
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Develop new data architecture and database processes to improve performance using AWS analytical services and lakehouse architecture patterns.
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Use Python, PySpark, SQL, and automation scripts to support data processing, validation, migration, and operational workflows.
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Ensure database security and compliance with HIPAA, GDPR, access control, auditability, encryption, and data governance requirements.
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Manage production operations including recurring reports, pipeline support, issue triage, change management, and release coordination.
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Communicate with stakeholders, mentor engineers, perform code reviews, and maintain strong relationships with cross-functional collaborators.
Knowledge, Skills, and Abilities:
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Strong understanding of data lake, data warehouse, and lakehouse architecture patterns on AWS.
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Client-focused approach with strong interpersonal, documentation, communication, and technical leadership skills.
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Ability to multitask, prioritize, manage production issues, and maintain attention to detail in complex data environments.
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Strong logical, analytical thinking, root-cause analysis, and problem-solving capabilities.
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Proficiency in Python, PySpark, SQL scripting, Shell scripting, and automation for database and data platform operations.
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Expert-level SQL and relational database design including schema design, normalization, dimensional modeling, and query optimization.
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Deep knowledge of PostgreSQL performance tuning, indexing, partitioning, stored procedures/functions, replication, backup, and recovery.
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Strong hands-on experience with PostgreSQL, Redshift, SQL Server, Athena, DynamoDB, RDS, S3, and AWS data services.
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Strong performance tuning experience across PostgreSQL, Redshift, Athena, DynamoDB, SQL Server, and SQL-based workloads.
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Familiarity with server environments, database connectivity, data movement, security, governance, and compliance standards.
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Experience with validated systems, healthcare data, EMR integrations, clinical trial data, and regulated production environments preferred.
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Project management, Agile delivery, GitHub workflows, Jira tracking, documentation, code review, and leadership experience.
Must Have Skills:
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Expert-level SQL expertise and relational database design experience.
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Advanced SQL Server experience including database development, optimization, stored procedures, SSIS, SSRS, and operational support.
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Strong hands-on PostgreSQL and Redshift experience including administration, tuning, data modeling, backup, recovery, and production operations.
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Experience building AWS data lake, data warehouse, and cloud BI solutions using S3, Redshift, Athena, DynamoDB, RDS, and related services.
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Experience with data architecture, scalable data processing frameworks, ETL patterns, lakehouse design, and production-grade data pipelines.
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Data modeling, database design, source-to-target mapping, data lineage, data dictionary, and technical documentation experience.
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Strong problem-solving, analytical, troubleshooting, production support, and incident management skills.
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Excellent documentation, communication, stakeholder management, and cross-functional collaboration abilities.
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Performance tuning and query optimization across PostgreSQL, Redshift, Athena, SQL Server, and large SQL workloads.
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Leadership, mentoring, code review, database standards, and technical decision-making capabilities.
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Jira, GitHub, Agile methodology, CI/CD awareness, release management, and delivery tracking experience.
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Experience implementing data quality frameworks, pipeline monitoring, alerting, reconciliation, and production support processes.
Good to Have Skills:
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Experience with MySQL, Oracle, Aurora PostgreSQL, RDS, and additional relational or NoSQL database platforms.
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Scripting and automation skills using Python, Shell, SQL automation, or AWS SDKs.
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Validated system experience and regulated SDLC documentation practices.
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Familiarity with Tableau, Power BI, or reporting and BI consumption patterns is helpful.
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Familiarity with Databricks, Snowflake, Glue, Lake Formation, Step Functions, Lambda, or modern lakehouse tooling.
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Experience with machine learning, NLP, LLMs, AI-assisted documentation, mapping automation, vector databases, embeddings, or LLM-enabled data quality is an advantage.
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Healthcare, Electronic Medical Records, claims data, clinical trial data, HIPAA, GDPR, and patient data domain experience.
Working Hours:
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India: 05:30 PM to 02:30 AM IST
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Philippines: 08:00 PM to 05:00 AM PHT
