Data Engineering Lead(8+ Years of Exp)

Embark on an exciting journey into the realm of software product development with 3Pillar! We extend an invitation for you to join our team and gear up for a thrilling adventure. At 3Pillar, our focus is on leveraging cutting-edge technologies that revolutionize industries by enabling data driven decision making. As a Lead, Data Engineering, you will hold a crucial position within our dynamic team, actively contributing to thrilling projects that reshape data analytics for our clients, providing them with a competitive advantage in their respective industries.If your passion for data engineering solutions that make a real-world impact, consider this your pass to the captivating world of Data Science and Engineering! 🌍🔥 Skills Set

8+ years of experience in Data Engineering or related field, including 2+ years in a lead role.

Expert-level proficiency with AWS data services (e.g., Glue, EMR, Lambda, Redshift, S3, Kinesis, Step Functions).

Strong Python skills for data processing, automation, and pipeline development.

Experience building batch and streaming pipelines (Spark, PySpark, Kafka, Kinesis, etc.).

Strong SQL expertise and experience with relational and NoSQL databases.

Hands-on experience with IaC (Terraform, CloudFormation, CDK).

Familiarity with DevOps tools for CI/CD (e.g., GitHub Actions, GitLab CI, Jenkins).

Understanding of data modeling, data warehousing concepts, and distributed systems.

Key Responsibilities

Understanding the client requirement and lead creation of robust data platforms on multiple cloud technologies.

Lead end-to-end analytics projects — from requirement gathering to deployment.

Mentor junior team members, perform code reviews, and ensure adherence to quality standards.

Work closely with cross-functional teams (engineering, product, business) to align data strategy with business goals.

Develop and optimize complex SQL queries, LookML models, dashboards, and reports.

Troubleshoot performance bottlenecks, data inconsistencies, and integration challenges.

Stay up to date with advancements in the analytics and cloud data ecosystem.

Drive innovation in reporting and analytics capabilities.

Recommend and implement new tools, frameworks, and practices to enhance the data platform.

Define best practices, coding standards, and governance for analytics projects.

Monitor data systems performance and implement optimization strategies.

Ensure data accuracy, integrity, privacy, security, and compliance through quality control procedures.