Data Engineer

About the Job

Our client is a high-growth, data-centric technology enterprise managing high-velocity data transactions for millions of global users. We are seeking an analytical, systems-minded Data Engineer to serve as a key architect behind our enterprise core data platform. In this role, you will be responsible for designing, building, and maintaining the robust data pipelines and cloud infrastructures that ingest millions of daily events. You will ensure that raw data is transformed into clean, highly optimized, and democratized structures, empowering downstream data scientists, analysts, and production AI models with flawless information.

Why Join Us

• Enterprise Data Scale: Move past standard data warehouses to engineer pipelines that ingest, parse, and process petabyte-scale data lakes running in real time.
• Modern Cloud Data Stack: Deepen your technical expertise by working directly with premier distributed systems and data tooling, including Apache Spark, Airflow, Snowflake, Kafka, and DBT.
• Direct Business Impact: You will own the foundation of our data strategy. Your pipeline optimizations will directly reduce cloud computing costs and unlock real-time business critical reporting.

Responsibilities

• Pipeline Engineering (ETL/ELT): Architect, write, and deploy resilient batch and streaming data pipelines using Python or Scala to extract, transform, and load data from transactional databases, webhooks, and third-party SaaS APIs.
• Data Warehouse & Lakehouse Optimization: Design and optimize schemas, tables, and partitions within cloud data warehouses (e.g., Snowflake, BigQuery, Redshift) to reduce query latencies and maximize storage efficiency.
• Data Quality & Observability Automation: Implement automated testing frameworks and monitoring alerts (using tools like Great Expectations or Datadog) to proactively detect data drift, schema changes, and pipeline anomalies.
• Workflow Orchestration Management: Author, schedule, and maintain complex, dependency-aware Directed Acyclic Graphs (DAGs) using workflow management tools like Apache Airflow, Prefect, or Dagster.
• Data Modeling & Governance: Collaborate closely with backend engineers and analytics teams to design logical data models (e.g., Star Schema, Data Vault 2.0), enforce data governance policies, and ensure strict compliance with global security standards.

Requirements

• Education & Engineering Literacy: Bachelor’s degree in Computer Science, Computer Engineering, Information Systems, or an equivalent technical discipline with a strong emphasis on distributed systems and databases.
• Professional Experience: 2 to 5 years of proven professional experience as a dedicated data engineer or backend platform developer operating in a production cloud environment.
• Advanced SQL Command: Expert-level mastery of SQL, with a deep understanding of query optimization, window functions, complex indexing, and profiling execution plans.
• Programmatic Software Foundations: Strong proficiency writing clean, testable production code in Python, Scala, or Java, applying object-oriented or functional programming principles.
• Cloud Infrastructure Fluency: Practical, hands-on experience utilizing core services within major cloud ecosystems—specifically AWS or GCP (e.g., S3/GCS, EC2/Compute Engine, IAM, Cloud Functions).
• Distributed Computing Exposure: Foundational knowledge of distributed data processing concepts and storage architectures (e.g., Apache Spark, Hadoop, Parquet formats).

Preferred Qualifications

• Prior experience utilizing data build tools for managing analytics engineering transformations.
• Hands-on experience with streaming architectures and message brokers (such as Apache Kafka, Amazon Kinesis, or RabbitMQ).
• Familiarity with infrastructure-as-code software (Terraform) and container orchestration tools (Docker, Kubernetes).

Job Details

• Employment Type: Full-Time, Permanent
• Salary Range: $110,000 – $145,000 base structure + Performance Bonus, Equity Shares & Premium Corporate Health, Wellness, and Remote Work Perks.