Data Lake / ETL Engineer

Data Lake / ETL Engineer – 2+ Years

Role Overview

We are seeking a Data Lake / ETL Engineer with 2+ years of experience in designing, developing, and maintaining data pipelines and ETL/ELT processes. The role focuses on building and optimizing data ingestion, transformation, and storage solutions that enable business analytics, AI/ML use cases, and secure enterprise data lakes.

Key Responsibilities

• Pipeline Development

  • Build and maintain ETL/ELT pipelines for structured and semi-structured data.

  • Support data ingestion from databases, APIs, streaming platforms, and flat files.

  • Ensure data quality, integrity, and lineage across data flows.

• Data Lake Engineering

  • Assist in the design and development of data lake solutions on cloud and on-prem.

  • Implement storage and retrieval mechanisms optimized for performance.

  • Manage metadata and cataloging for discoverability and governance.

• Performance & Optimization

  • Tune ETL workflows for efficiency and cost-effectiveness.

  • Implement partitioning, indexing, and caching for large-scale data processing.

  • Automate repetitive data preparation tasks.

• Collaboration & Support

  • Work with data scientists and analysts to deliver clean and reliable datasets.

  • Collaborate with senior engineers on best practices for data modeling and pipeline design.

  • Provide L2 support for production pipelines and help troubleshoot failures.

Required Skills & Experience

• 2+ years of experience in data engineering or ETL development.

• Proficiency in SQL and Python (or Scala/Java) for data transformations.

• Hands-on with ETL tools (Informatica, Talend, dbt, SSIS, Glue, or similar).

• Exposure to big data technologies (Hadoop, Spark, Hive, Delta Lake).

• Familiarity with cloud data platforms (AWS Glue/Redshift, Azure Data Factory/Synapse, GCP Dataflow/BigQuery).

• Understanding of workflow orchestration (Airflow, Oozie, Prefect, or Temporal).

Preferred Knowledge

• Experience with real-time data pipelines using Kafka, Kinesis, or Pub/Sub.

• Basic understanding of data warehousing and dimensional modeling.

• Exposure to containerization and CI/CD pipelines for data engineering.

• Knowledge of data security practices (masking, encryption, RBAC).

Education & Certifications

• Bachelor’s degree in Computer Science, IT, or related field.

• Preferred certifications:

  • AWS Data Analytics – Specialty / Azure Data Engineer Associate / GCP Data Engineer.

  • dbt or Informatica/Talend certifications.

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