Senior Data Engineer

Overview

Position Summary:

As a Sr Data Engineer you will create production data pipelines for our advanced analytics and data science teams – as well as collaborate with other technical personnel on internal & external data sources and infrastructure needs. The Data Engineer will design, evaluate, and test data infrastructures and be a subject matter expert for all things data across the organization.

 

Geo-Salary Information

State specific pay scales for this role are as follows:

$105,245 to $199,452 (CA, NJ, NY, WA, HI, AK, MD, CT, RI, MA)

$95,677 to $181,320 (NV, OR, AZ, CO, WY, TX, ND, MN, MO, IL, WI, FL, GA, MI, OH, VA, PA, DE, VT, NH, ME)

$86,110 to $163,188  (UT, ID, MT, NM, SD, NE, KS, OK, IA, AR, LA, MS, AL, TN, KY, IN, SC, NC, WV)

The expected base salary for this position will vary depending on a number of factors, including relevant experience, skills and location.

Responsibilities

Essential Job Functions:

• • Design, build, and launch collections of high-quality big data/data lake solutions on Cloud platform preferably AWS, Snowflake that support multiple use cases across all departments, all products, and all states.
  • Solve our most challenging data integration problems, utilizing optimal ETL patterns, frameworks, query techniques, sourcing from structured and unstructured data sources.
  • Assist in owning existing processes running in production, optimizing complex code through advanced algorithmic concepts.
  • The Data Engineer is an expert in all datalakes, data warehouses, and data cubes within Mercury, with no gaps in knowledge. Can efficiently and accurately extract and manipulate data from any source. 
  • Collaborate with teams of data analysts and data scientists, who research and integrate algorithms to develop solutions to address complex data problems. Influence all functions across the organization to identify data opportunities to drive profitable growth. Proactively identify pain points that Analytics & Data Science face with our existing data models.
  • Leverages existing data infrastructure to fulfill all data-related requests, perform necessary data housekeeping, data cleansing, normalization, hashing, and implementation of required data model changes. Analyzes data to spot anomalies, trends and correlate similar data sets. Designs, develops and implements natural language processing software modules.
  • Other functions may be assigned

 

Qualifications

Education:

• Bachelor’s degree in computer engineering, Computer Science, Mathematics, Electrical Engineering, Information Systems, or related field
• Actuarial experience/exams preferred.
• Or equivalent combination of education and/or experience

 

 

Experience:

• 6 or more years of experience in data analytics, data engineering, and/or data science
• 5 or more years of experience in architecting/designing and leading development of big data/data lake solutions on Cloud platforms, preferably AWS (S3, Glue/EMR, Athena, AppFlow) or Snowflake
• 6 or more years of experience in Python, Java and/or Scala programming
• 5 or more years of experience in writing SQL statements and query performance tuning
• 5 or more years of experience in RDMS or MPP databases, preferably AWS Redshift or Snowflake

 

 

Preferred Experience:

• 5 or more years of experience working with MDM (Master Data Management), preferably Reltio
• 1 or more years of experience working with AI technologies in a production environment
• 5 or more years of experience with ETL tools, preferably DBT
• 3 or more years of experience in the P&C insurance industry 

 

Knowledge and Skills:

• A high-level specialist who regularly interacts and works with senior management.
• Expert at analyzing source data to identify gaps and inconsistencies
• Able to multitask, prioritize, and manage time effectively.
• The ability to think conceptually, analytically and creatively comfortable with ambiguity.
• Demonstrated expert skills in design, develop, and implement MDM solutions using industry-standard platforms.
• Demonstrated expert skills in managing and maintaining golden records for customer, product, vendor, and other master data domains.
• Collaborate with data stewards and business teams to define data quality rules, validation processes, and governance workflows.
• Ensure data deduplication, matching, merging, and survivorship logic is implem