Enterprise Data Lake (EDL) Technical Lead

Own the design and implementation of enterprise-scale data lake solutions on Azure/AWS Cloud 

Define technical standards and best practices for data platform components 

Drive technical decision-making across data ingestion, storage, processing, and governance layers 

Provide technical mentorship and guidance to data engineering and platform teams 

Collaborate with Architecture team on overall architectural alignment and strategy 

Partner with Product Management, Data Engineering, Security, SRE, and downstream consumers 

Own the design and implementation of enterprise data catalog and metadata management layer 

Build and operate data catalog using DataHub or equivalent metadata platform 

Design and implement automated metadata ingestion, lineage tracking, and data discovery capabilities 

Design and implement data governance policies, data quality rules, and compliance frameworks 

Enable self-service data discovery and access management for downstream consumers 

Establish data ownership, stewardship models, and metadata standards across the enterprise 

Build APIs and services for metadata-driven data operations and workflows 

Design and implement real-time and batch data ingestion pipelines using Apache Kafka 

Optimize data processing workflows on Databricks platform (Delta Lake, Spark optimization, Unity Catalog) 

Design integration patterns between RDBMS sources and data lake (CDC, batch ETL, replication) 

Optimize database performance, query tuning, and indexing strategies across relational and distributed systems 

Implement infrastructure as code using Terraform for automated provisioning and management 

Design and deploy containerized data services on Kubernetes clusters 

Develop data platform services and tooling using C# and Go 

Build database migration tools, data validation frameworks, and connectivity services 

Ensure platform scalability, reliability, and security across all data lake components 

Implement monitoring, logging, and observability solutions for data infrastructure 

Optimize Linux-based systems for data processing workloads 

Establish CI/CD pipelines for data platform deployments 

Ensure platform security, compliance, and data governance standards (GDPR, SOC2, etc.) 

Drive cost optimization and performance tuning initiatives 

Implement database backup/recovery strategies and disaster recovery planning 

5+ years with Apache Kafka (streaming architecture, Kafka Connect, Schema Registry, stream processing) 

3+ years with Databricks (Delta Lake, Apache Spark optimization, Unity Catalog, cluster management) 

Deep understanding of data lake architecture patterns (Bronze/Silver/Gold, medallion architecture) 

3+ years with DataHub or similar metadata management platforms (Alation, Collibra, Apache Atlas) 

Deep experience building and operating enterprise data catalog systems 

Expertise in automated metadata extraction, lineage tracking, and impact analysis 

Experience with data quality frameworks and metadata-driven data operations 

Knowledge of data governance policies, data classification, and compliance automation 

Understanding of data discovery, access control, and self-service analytics enablement 

5+ years with enterprise RDBMS platforms including: 

SQL Server (T-SQL, SSIS, SSRS, replication, Always On Availability Groups) 

PostgreSQL (advanced query optimization, partitioning, extensions, streaming replication) 

MySQL (replication, clustering, performance tuning) 

Strong SQL skills (complex queries, stored procedures, window functions, query optimization) 

Database design principles (normalization, indexing strategies, schema design, partitioning) 

Change Data Capture (CDC) patterns and implementation (Debezium, Azure Data Factory, AWS DMS, custom solutions) 

Database migration experience (schema migration, data migration, zero-downtime migrations) 

5+ years with Azure or AWS Cloud including: 

Azure: Data Lake Storage (Gen2), Event Hubs, AKS, Azure SQL, Key Vault, Azure AD, Monitor 

AWS: S3, MSK/Kinesis, EKS, RDS/Aurora, Secrets Manager, IAM, CloudWatch 

Cloud-native data services, networking, security, and IAM 

3+ years with Kubernetes (deployment strategies, scaling, monitoring, service mesh, Helm) 

3+ years with Terraform (modules, state management, multi-environment deployments, multi-cloud) 

Strong proficiency in any programming languages like C#, Go 

Expert-level SQL across multiple database platforms 

Experience with Python for data engineering tasks (preferred) 

Familiarity with Shell scripting (Bash) 

7+ years in data engineering, platform engineering, or database engineering roles 

3+ years in technical leadership capacity (Tech Lead, Principal Engineer) 

Proven track record of delivering large-scale data infrastructure projects 

Experience leading teams of 5-10+ engineers 

Strong architectural design and system thinking capabilities 

Experience migrating legacy RDBMS workloads to modern data lake architectures 

Demonstrated ability to balance technical excellence with business needs 

Experience with cloud ETL services (Azure Data Factory, AWS Glue, Azure Stream Analytics, AWS Lambda) 

Knowledge of managed database features (Azure SQL elastic pools/hyperscale, AWS RDS/Aurora serverless) 

Knowledge of additional streaming technologies (Apache Flink) 

Experience with database sharding and horizontal partitioning strategies at scale 

Familiarity with NoSQL databases (Cosmos DB, DynamoDB, MongoDB, Cassandra, Redis) 

Experience with Apache Iceberg 

Knowledge of data observability tools 

Education: Any Bachelors or Similar