Data Engineer I

SDE 1 – Data Platform Engineer

Overview

Software Development Engineer (SDE 1) – Data Platform

In this role, you’ll design, build, and optimize data systems and pipelines that fuel analytics, reporting, and data-driven decision-making across the organization. You’ll work on modern data platforms, collaborate with experienced engineers, and gain hands-on exposure to cutting-edge data and cloud technologies.

This opportunity is ideal for someone who enjoys solving complex data problems, working with large-scale systems, and continuously learning in a fast-paced, high-impact environment.

What You’ll Do

Data Pipelines & Infrastructure

• Design and develop scalable

  ETL/ELT pipelines

  and workflows using orchestration tools such as

  Airflow, Azkaban, or Luigi

  .
• Build

  batch and real-time data processing systems

  using technologies like

  Apache Spark, Flink, Kafka, and Debezium

  .
• Implement

  data quality, observability, and lineage frameworks

  to ensure reliable, accurate, and trusted data delivery.

Data Management & Optimization

• Develop and maintain

  data ingestion, transformation, and storage layers

  using modern data lake and warehouse platforms (

  AWS S3, Redshift, Snowflake, Databricks

  ).
• Write and optimize

  SQL queries

  and data models to support analytical and reporting workloads.
• Collaborate with senior engineers to enhance

  system performance, scalability, and cost efficiency

  .

Collaboration & Delivery

• Partner with

  data analysts, ML engineers, and product teams

  to translate business needs into scalable data solutions.
• Work closely with

  platform and DevOps teams

  to ensure secure, efficient, and automated data operations.
• Participate in

  code reviews

  , maintain high-quality documentation, and follow

  software engineering best practices

  .

Continuous Learning

• Stay updated with emerging

  data technologies, frameworks, and cloud solutions

  .
• Contribute to initiatives in

  data standardization, governance, and automation

  to strengthen the organization’s data foundation.

What You’ll Bring

Technical Skills :

• Strong understanding of

  data engineering fundamentals

  — ETL/ELT design, data modeling, warehousing, and lake architectures.
• Hands-on experience with one or more data technologies such as

  Spark, Kafka, or Airflow

  .
• Proficiency in

  Python, Java, or Scala

  for data engineering and automation tasks.
• Strong

  SQL skills

  and familiarity with relational databases (e.g.,

  MySQL, PostgreSQL

  ).
• Exposure to

  cloud environments

  , preferably

  AWS

  , including services like

  S3, Glue, Redshift, EMR

  .

Foundational Knowledge :

• Understanding of

  software engineering practices

  — version control (Git), testing, CI/CD, and documentation.
• Basic awareness of

  data governance, observability, and quality monitoring

  concepts.

Soft Skills :

• Strong

  analytical thinking and problem-solving

  mindset.
• Collaborative and eager to learn from experienced mentors.
• Detail-oriented, reliable, and driven to build scalable, high-performance systems.

Preferred Qualifications :

• Experience or exposure to

  data lakehouse architectures

  .
• Familiarity with

  containerization (Docker)

  and

  orchestration (Kubernetes)

  .
• Understanding of

  MLOps, data observability, or data catalog frameworks

  .
• Prior experience in

  high-growth, consumer-tech, or digital-first environments

  .