Senior Data Engineer

Job Title: Senior Data Engineer

Location: Remote / Hybrid / Onsite

Department: Data Platform / Engineering

Employment Type: Full-time

Experience Required: 4+ years (strong hands-on ownership required)

Role Overview

We are looking for a Senior Data Engineer with at least 4 years of deep, hands-on experience building and operating production-grade data systems. This role demands strong individual contribution, sound architectural judgment, and the ability to own complex data pipelines end-to-end.

You will work on scalable data platforms supporting analytics, reporting, and machine learning use cases, and will be expected to uphold high engineering standards across reliability, performance, and maintainability.

Key Responsibilities

Design, build, and maintain reliable, scalable data pipelines (batch and streaming)

Develop and optimize ETL/ELT workflows with a strong focus on performance and cost efficiency

Build and maintain data warehouses and data lakes

Ensure data quality, consistency, and availability across systems

Implement monitoring, alerting, and failure recovery mechanisms

Collaborate with data scientists, analysts, and backend engineers

Perform code reviews and contribute to data engineering best practices

Participate in architecture discussions and technical design reviews

Troubleshoot production data issues and drive root-cause resolution

Mandatory Qualifications

4+ years of hands-on experience in data engineering or backend engineering with a strong data focus

Strong proficiency in Python and SQL (Scala or Java is a plus)

Solid experience with distributed data processing frameworks (Apache Spark preferred)

Hands-on experience with ETL orchestration tools (Airflow or equivalent)

Experience working with cloud data platforms (AWS, GCP, or Azure)

Strong understanding of data modeling (dimensional and normalized models)

Experience building and maintaining production-grade data pipelines

Familiarity with data versioning, schema evolution, and backward compatibility

Advanced Technical Expectations

Experience with streaming or near-real-time data processing (Kafka, Kinesis, Pub/Sub)

Strong SQL optimization and query performance tuning skills

Hands-on experience with modern data warehouses (Snowflake, BigQuery, Redshift)

Experience with data quality checks, validations, and observability

Exposure to Infrastructure as Code (Terraform preferred)

Experience with CI/CD pipelines for data workloads

Engineering Quality & Ownership

Writes clean, testable, and well-documented data code

Understands trade-offs between cost, latency, and reliability

Owns data services and pipelines in production, including on-call support if required

Contributes to improving platform stability and scalability

Demonstrates strong debugging and problem-solving skills

Nice-to-Have

Experience with CDC pipelines

Exposure to lakehouse architectures (Delta Lake, Iceberg, Hudi)

Familiarity with Docker and Kubernetes

Experience supporting ML data pipelines

Prior experience mentoring junior engineers

What Success Looks Like

Pipelines are reliable, observable, and scalable

Data is trusted by analytics and business teams

Performance and cost optimizations are delivered consistently

Clear documentation and maintainable designs are produced

Minimal production incidents caused by data issues

Compensation & Benefits

Competitive salary and performance incentives

Health and wellness benefits

Flexible work arrangements

Learning budget and long-term career growth opportunities

Keywords

Senior Data Engineer, Data Engineering, Data Pipelines, ETL, ELT,

Apache Spark, Kafka, Distributed Systems, Cloud Data Platforms,

AWS, Azure, GCP, Data Warehousing, Snowflake, BigQuery, Redshift,

SQL, Python, Airflow, CI/CD, Data Quality, Data Observability

Hashtags

#SeniorDataEngineer #DataEngineering #DataPipelines #ETL #ELT

#ApacheSpark #Kafka #DistributedSystems #BigData

#CloudEngineering #AWS #Azure #GCP #DataWarehouse

#SQL #Python #Airflow #DataPlatforms