Lead Data Engineering

Lead Data EngineeringTechnical skill sets: Python, AWS- S3, Lambdas, Glue, Gen-Ai, LLMs, SQL, DynamoDB, Kafka/Kinesis, pySparkResponsibilities:1. Advanced Architecture & System DesignA Tech Lead is primarily responsible for the overall platform vision and ensuring systems do not break under scale. Distributed Computing: Mastery of frameworks like Apache Spark or Ray for massive-scale parallel data processing. Streaming & Event-Driven Architecture: Deep understanding of real-time pipeline design using Kafka, Kinesis, or Flink. Cloud Infrastructure: Expertise in at least one major public cloud (AWS), specifically understanding storage/compute decoupling and cost optimization.2. Core Programming & Database ManagementLeads set coding standards and review code, requiring complete fluency in the fundamentals. SQL: Advanced mastery for metrics computation, window functions, and query performance tuning across relational and columnar databases (e.g., Snowflake, Redshift, BigQuery). Scripting Languages: High proficiency in Python or Scala for writing reusable pipeline code and interacting with APIs. Data Storage: Deep familiarity with both columnar/analytical stores and NoSQL databases (e.g., DynamoDb, Cassandra).3. Pipeline Orchestration & DevOpsEnsuring pipelines run smoothly, idempotently, and securely in production. Workflow Orchestration: Ability to architect Directed Acyclic Graphs (DAGs) in tools like Apache Airflow or Prefect. CI/CD & Infrastructure as Code (IaC): Applying software engineering principles to data by using Docker, Kubernetes, and Terraform. Data Governance & Security: Implementing Role-Based Access Control (RBAC), data masking, and compliance frameworks.4. Leadership & Soft SkillsTech leads also mentor junior engineers, estimate project timelines, and translate ambiguous business needs into concrete technical specifications. Mentorship & Code Review: Fostering a collaborative development environment and enforcing style guidelines. System Observability: Building logging, monitoring, and alerting mechanisms so the team knows exactly when and why pipelines fail.