Technical skill sets :
Python, Pyspark, AWS Lambda, S3,AWS glue, Kinesis,SQL, Apache Flink, Confluent Kafka, AI-LLM, Gen-AI , Java is optional
Responsibilities :
1. Advanced Architecture & System Design
A 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 Management
Leads 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 & DevOps
Ensuring 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 Skills
Tech 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.