Job Description: Big Data Engineer Summary
We are looking for an experienced Big Data Engineer with 7–10 years of hands-on experience to design, build, and maintain scalable data pipelines and processing systems in an on-premises Big Data environment. Beyond strong individual contribution, the ideal candidate will own architecture and design decisions, set technical direction, and mentor and support other developers on the team. The role works closely with cross-functional teams to deliver reliable, high-quality data solutions that support business and analytics needs.
Roles & Responsibilities
- Lead the design, development, and maintenance of robust, scalable data pipelines for ingestion, transformation, and processing of large datasets in an on-premises environment.
- Own architectural and design decisions for data solutions, evaluating trade-offs and defining technical standards for the team.
- Mentor, guide, and support other data engineers through code reviews, design reviews, technical coaching, and hands-on problem-solving.
- Build and optimize data workflows using Python, Spark, and the Hadoop ecosystem.
- Work extensively with Hadoop ecosystem components (Hive, HDFS, Impala) to manage and query large-scale data.
- Manage and optimize batch scheduling and job orchestration using enterprise schedulers such as CA7 or Control-M.
- Ensure data quality, integrity, and performance across data platforms.
- Collaborate with data analysts, data scientists, and business stakeholders to translate data requirements into sound technical designs.
- Troubleshoot and resolve complex issues in data pipelines and production environments, acting as an escalation point for the team.
- Champion best practices for coding standards, version control, testing, and documentation.
- Stay current with emerging technologies, particularly AI/ML capabilities, and identify opportunities to apply them to data engineering workflows.
Technical Skills Must Have
- 7–10 years of overall experience in data engineering, with a proven track record in technical leadership (design ownership, mentoring, guiding development teams).
- Python – strong hands-on development experience building production-grade data solutions.
- Big Data / Hadoop ecosystem (Hadoop, Hive, Impala, HDFS) – deep, hands-on experience in on-premises environments.
- Apache Spark – solid experience developing and tuning large-scale distributed data processing jobs.
- Job scheduling / orchestration – hands-on experience with CA7 or Control-M (or comparable enterprise schedulers).
- Strong understanding of data structures, ETL processes, and SQL.
- Extensive experience with large-scale data processing and distributed systems.
- Demonstrated ability to make sound architecture/design decisions and to mentor and support other developers.
- Exposure to AI/ML concepts or tools, with a strong willingness to learn and grow in this space.
Roles & Responsibilities
- Lead the design, development, and maintenance of robust, scalable data pipelines for ingestion, transformation, and processing of large datasets in an on-premises environment.
- Own architectural and design decisions for data solutions, evaluating trade-offs and defining technical standards for the team.
- Mentor, guide, and support other data engineers through code reviews, design reviews, technical coaching, and hands-on problem-solving.
- Build and optimize data workflows using Python, Spark, and the Hadoop ecosystem.
- Work extensively with Hadoop ecosystem components (Hive, HDFS, Impala) to manage and query large-scale data.
- Manage and optimize batch scheduling and job orchestration using enterprise schedulers such as CA7 or Control-M.
- Ensure data quality, integrity, and performance across data platforms.
- Collaborate with data analysts, data scientists, and business stakeholders to translate data requirements into sound technical designs.
- Troubleshoot and resolve complex issues in data pipelines and production environments, acting as an escalation point for the team.
- Champion best practices for coding standards, version control, testing, and documentation.
- Stay current with emerging technologies, particularly AI/ML capabilities, and identify opportunities to apply them to data engineering workflows.
- 7–10 years of overall experience in data engineering, with a proven track record in technical leadership (design ownership, mentoring, guiding development teams).
- Python – strong hands-on development experience building production-grade data solutions.
- Big Data / Hadoop ecosystem (Hadoop, Hive, Impala, HDFS) – deep, hands-on experience in on-premises environments.
- Base Compensation Range: $100,000- $130,000
- The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.
Skills Required
- 7-10 years of data engineering experience
- Proven technical leadership, architecture/design ownership, and mentoring experience
- Python development for production-grade data solutions
- On-premises Big Data / Hadoop ecosystem experience (Hadoop, Hive, Impala, HDFS)
- Apache Spark development and tuning of distributed jobs
- Enterprise job scheduling/orchestration experience (CA7 or Control-M)
- Strong SQL, ETL processes, and data structures knowledge
- Extensive experience with large-scale data processing and distributed systems
- Exposure to AI/ML concepts or tools and willingness to learn
What We Do
Choosing a digital partner is about more than capabilities — it’s about collaboration and character. Unrealistic overhauls and off-the-shelf products ignore what matters most — your unique needs, culture, goals, and your legacy data and technology environments. At EXL, our collaboration is built on ongoing listening and learning to adapt our methodologies. We’re your business evolution partner—tailoring solutions that make the most of data to make better business decisions and drive more intelligence into your increasingly digital operations. Whether your goals are scaling the use of AI and digital, redesign operating models, or driving better and faster decisions, we’re here to partner with you to help you gain—and maintain—competitive advantage with efficient, sustainable models at scale. Our expertise in transformation, data science, and change management helps make your business more efficient and effective, improve customer relationships and enhance revenue growth. Instead of focusing on multi-year, resource- and time-intensive platform designs or migrations, we look deeper at your entire value chain to integrate strategies with impact. We use our specialization in analytics, digital interventions, and operations management—alongside deep industry expertise — to deliver solutions that help you outperform the competition. At EXL, it’s all about outcomes—your outcomes—and delivering success on your terms. Share your goals with us and together, we’ll optimize how you leverage data to drive your business forward. For more information, visit www.exlservice.com.
