Sr. Data Engineer

Key Responsibilities:

• Architect & Lead: Design and implement scalable data architectures, including data lakes, warehouses, and streaming solutions.
• End-to-End Ownership: Own pipelines from ingestion to serving layers, ensuring reliability, observability, and cost-efficiency.
• Data Modeling: Define and maintain robust data models to support analytics, machine learning, and operational reporting.
• Mentorship & Leadership: Guide junior and mid-level engineers through code reviews, design discussions, and best practices.
• Performance Optimization: Identify bottlenecks, tune pipelines, and improve query performance at scale.
• Governance & Security: Implement and enforce data quality checks, access control, and compliance with privacy regulations (GDPR, CCPA).
• Collaboration: Work closely with product, engineering, and business stakeholders to translate requirements into scalable data solutions.
• Innovation: Evaluate and introduce new technologies and tools to improve the data platform’s reliability and developer experience.

Requirements

• Experience: 5+ years in data engineering or backend engineering, with a track record of leading large-scale data projects.
• Programming Expertise: Advanced skills in Python (or Scala/Java) and strong proficiency in SQL.
• Big Data Frameworks: Deep experience with Spark, Flink, Beam, or similar distributed data processing engines.
• ETL/ELT Orchestration: Expertise with Airflow, dbt, Dagster, or similar tools.
• Cloud Platforms: Strong experience with at least one major cloud provider (AWS, GCP, Azure) — including data services like Snowflake, BigQuery, Redshift, Databricks.
• Data Modeling: Skilled at designing data schemas (OLTP, OLAP, dimensional models).
• Streaming Data: Experience with Kafka, Kinesis, or Pub/Sub for real-time pipelines.
• DevOps Mindset: Familiarity with CI/CD, infrastructure-as-code (Terraform, CloudFormation), and monitoring/alerting.

Skills Required:

• Computer / Software / It / Data