Senior Data Engineer

Position Requirements:

Minimum 4+ years of experience on PostgreSQL or any Unix based relational database. Advanced understanding of SQL.

Minimum 2+ years experience with Pentaho Data Integration Tools or any ETL/ELT tools like Talend, Informatica, or HOP.

2+ years of hands-on experience with orchestration tools (Airflow, Control-M, Autosys).

Hands-on experience in Python scripting for automation and data processing (1+ years).

Proven experience with cloud/data platforms (Snowflake, Redshift, BigQuery) and big data (Hadoop, Hive, Spark) is good to have.

Strong knowledge of data architecture (dimensional modeling, pipelines, warehousing).

Experience collaborating with non-technical teams (BI, Marketing, Finance) to translate business needs into technical solutions.

Experience with reporting and Data Visualization platforms (Tableau, Pentaho BI) will be an added advantage.

Understanding of Digital Marketing Transactional Data (Click Stream Data, Ad Interaction Data, Email Marketing Data)

Experience with setting up the infrastructure and architectural requirements and Understanding of Medical/Clinical data.

Leadership: Ability to mentor junior engineers and lead small projects independently.

Strong communication and documentation skills.

Willingness to learn new technologies.

Preferred:

GCP/BigQuery, Airflow, or digital marketing data (clickstream, ad

interactions).

Exposure to Linux and scheduling tools.

Role & Responsibilities:

Design and build scalable ETL/ELT pipelines for structured/unstructured data (files, Postgres, Vertica, Hive).

Analyze Business Requirements, design and implement required data model and Build ETL/ELT strategies.

Lead data architecture and engineering decision making/planning.

Optimize data workflows (performance, cost, scalability) using orchestration tools like Airflow.

Collaborate cross-functionally to align data solutions with business goals (e.g., BI dashboards, marketing analytics).

Document and communicate technical designs to both technical and non-technical stakeholders.

Troubleshoot and resolve data pipeline issues with minimal supervision.

Mentor junior engineers and promote best practices in data engineering.