Data Engineer

LGL Technologies LLC; Cleveland (OH); On-site; USD 120,000 - 170,000; Full time

Job summary

LGL Technologies LLC in Ohio is seeking a senior data engineer to design, develop, and maintain ETL/ELT pipelines using Python and SQL from day one, onsite.

You will work with cloud platforms (AWS, Azure, GCP), data warehouses like Snowflake/Redshift/Azure Synapse/BigQuery, and big data tools (Spark, Hadoop, Kafka), and build robust CI/CD pipelines.

Candidates should have 10+ years of experience and be comfortable with Linux/Unix, relational databases, and workflow orchestration (Airflow).

Qualifications

• 10+ years of Python/SQL/ETL/ELT and data engineering experience.
• 5+ years with cloud platforms and data warehouses.
• 4+ years with Spark, Hadoop, Kafka.
• 3+ years with Airflow or equivalent workflow orchestration.

Responsibilities

• Design, develop, and maintain ETL/ELT pipelines using Python/SQL.
• Optimize relational databases and complex SQL queries.
• Leverage cloud data services and big data tech (Spark/Hadoop/Kafka).
• Build and optimize data models, data lakes, and data integration solutions.

Skills

Python

SQL

ETL/ELT Development

Apache Spark

Hadoop

Cloud Platforms (AWS, Azure, GCP)

CI/CD Pipelines

Linux/Unix

Kafka

Tools

Oracle

SQL Server

PostgreSQL

MySQL

Apache Airflow

Snowflake

Redshift

Azure Synapse

Google BigQuery

Job description

LOCATION : OHIO

ONSITE POSITON FROM DAY 1

MUST HAVE 10+ YEARS OF EXPERIENCE in Python, SQL, ETL/ELT Development, Apache Spark, Hadoop, AWS / Azure / Google Cloud Platform (GCP), CI/CD Pipelines, Linux/Unix

Job Description

• 60 months of experience designing, developing, and maintaining ETL/ELT data pipelines using Python and SQL.
• 60 months of experience with relational databases such as Oracle, SQL Server, PostgreSQL, or MySQL, including complex SQL query development and performance tuning.
• 48 months of experience with cloud platforms such as AWS, Azure, or Google Cloud Platform and cloud-based data services.
• 48 months of experience with big data technologies including Apache Spark, Hadoop, and Kafka.
• 36 months of experience with data warehousing solutions such as Snowflake, Amazon Redshift, Azure Synapse, or Google BigQuery.
• 36 months of experience with workflow orchestration tools such as Apache Airflow or equivalent scheduling tools.
• 24 months of experience developing and optimizing data models, data lakes, and data integration solutions.