Senior Data Engineer

About Tredence:

Tredence focuses on last-mile delivery of powerful insights into profitable actions by uniting its strengths in business analytics, data science and software engineering. The largest companies across industries are engaging with us and deploying their prediction and optimization solutions at scale. Head quartered in the San Francisco Bay Area, we serve clients in the US, Canada, Europe, and South East Asia. We are seeking an experienced data scientist who apart from the required mathematical and statistical expertise also possesses the natural curiosity and creative mind to ask questions, connect the dots, and uncover opportunities that lie hidden with the ultimate goal of realizing the data's full potential.

Primary Roles and Responsibilities:

Developing Modern Data Warehouse solutions using Databricks and AWS/ Azure Stack

Ability to provide solutions that are forward-thinking in data engineering and analytics space

Collaborate with DW/BI leads to understand new ETL pipeline development requirements.

Triage issues to find gaps in existing pipelines and fix the issues

Work with business to understand the need in reporting layer and develop data model to fulfill reporting needs

Help joiner team members to resolve issues and technical challenges.

Drive technical discussion with client architect and team members

Orchestrate the data pipelines in scheduler via Airflow

Skills and Qualifications:

Bachelor's and/or master's degree in computer science or equivalent experience.

Must have total 6+ yrs. of IT experience and 3+ years experience in Data warehouse/ETL projects.

Deep understanding of Star and Snowflake dimensional modelling.

Strong knowledge of Data Management principles

Good understanding of Databricks Data & AI platform and Databricks Delta Lake Architecture

Should have hands-on experience in SQL, Python and Spark (PySpark)

Candidate must have experience in AWS/ Azure stack

Desirable to have ETL with batch and streaming (Kinesis).

Experience in building ETL / data warehouse transformation processes

Experience with Apache Kafka for use with streaming data / event-based data

Experience with other Open-Source big data products Hadoop (incl. Hive, Pig, Impala)

Experience with Open Source non-relational / NoSQL data repositories (incl. MongoDB, Cassandra, Neo4J)

Experience working with structured and unstructured data including imaging & geospatial data.

Experience working in a Dev/Ops environment with tools such as Terraform, CircleCI, GIT.

Proficiency in RDBMS, complex SQL, PL/SQL, Unix Shell Scripting, performance tuning and troubleshoot Databricks Certified Data Engineer Associate/Professional Certification (Desirable).

Comfortable working in a dynamic, fast-paced, innovative environment with several ongoing concurrent projects

Should have experience working in Agile methodology

Strong verbal and written communication skills.

Strong analytical and problem-solving skills with a high attention to detail.

Mandatory Skills: Python/ PySpark / Spark with Azure/ AWS Databricks