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.