{"id":3703576,"url":"https://alion.io/job/deloitte-data-engineer-2","title":"Data Engineer","company":{"id":272,"name":"Deloitte","domain":"deloitte.com","url":"https://alion.io/company/deloitte","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Avature","truth_index":{"grade":"A","score":100,"open_postings":7,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":12,"computed_at":"2026-11-06T05:58:05Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Alation","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Cortex","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"GDPR","optional":false},{"name":"Great Expectations","optional":false},{"name":"LLM","optional":false},{"name":"Prefect","optional":false},{"name":"pySpark","optional":false},{"name":"RAG","optional":false},{"name":"Scala","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Prometheus","optional":true},{"name":"Python","optional":true}],"status":"live","first_seen_at":"2026-11-03T17:29:23Z","employer_posted_date":null,"last_verified_at":"2026-11-03T17:29:23Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"About the Company\n\nHashedin by Deloitte© HASHEDIN BY DELOITTE 2025 | Page 1\n\nABOUT HASHEDIN We are software engineers who solve business problems with a Product Mindset for leading global organizations. By combining engineering talent with business insight, we build software and products that can create new enterprise value. The secret to our success is a fast-paced learning environment, an extreme ownership spirit, and a fun culture. WHY SHOULD YOU JOIN US With the agility of a start-up and the opportunities of an enterprise, every day at HashedIn, your work will make an impact that matters. So, if you are a problem solver looking to thrive in a dynamic fun culture of inclusion, collaboration, and high performance – HashedIn is the place to be! From learning to leadership, this is your chance to take your software engineering career to the next level. So, what impact will you make Visit us @ https://hashedin.com\n\nAbout the Role\n\nPOSITION Data Engineering\n\nLOCATION Bangalore/Hyderabad/Pune/Coimbatore\n\nEXPERIENCE 5 to 8 Years\n\nJOB TITLE: Data Engineering\n\nOVERVIEW OF THE ROLE: As a Tech Lead – Data Engineering, you will lead the design of, and stay hands-on in building and optimizing, secure, scalable data pipelines and lakehouse/warehouse platforms on Databricks or Snowflake that power business-critical analytics, data science, and AI applications. You will play a central role in setting technical direction and in implementing and automating batch and, where required, streaming data workflows using modern cloud technologies (AWS, GCP, or Azure). You will guide a team of data engineers, set engineering standards, and own technical delivery and quality. Working within cross-functional teams and with client stakeholders, you will deliver well-engineered, high-quality code and data models, enable AI-ready data for RAG, LLM, and ML use cases, build and deploy AI agents using Agent Bricks and Snowflake Cortex, and drive best practices for data reliability, lineage, quality, governance, cost efficiency, and security.\n\nResponsibilities\n\nKey Responsibilities:\n\nTechnical leadership and delivery\n\nLead architecture and technical design for data pipelines and platforms on Databricks or Snowflake, owning design reviews and documenting key decisions and trade-offs.\nLead a small team of data engineers: guide day-to-day work, allocate tasks, and uphold engineering standards through code and design reviews.\nOwn technical delivery: estimate, plan, and break down work, flag and mitigate technical risks, and ensure on-time, high-quality releases in an Agile setup.\nPartner with client and business stakeholders to translate requirements into technical solutions, and communicate designs, trade-offs, and status clearly.\n\nPipeline design and development\n\nDesign, build, and optimize scalable batch and streaming data pipelines and ETL/ELT workflows on Databricks or Snowflake, using Spark (PySpark/Scala), SQL, and orchestration tools such as Airflow, Prefect, or Databricks Workflows.\nArchitect efficient solutions for high-volume, batch and, where required, real-time streaming and event-driven data processing, leveraging best-in-class patterns and frameworks.\nArchitect and implement Medallion (Bronze/Silver/Gold) and lakehouse/warehouse architectures using Delta Lake, Delta Live Tables/Lakeflow, Snowpipe, Streams, Tasks, and Dynamic Tables to support analytics, data science, AI, and BI workloads.\nWrite performant, production-grade SQL for complex data aggregation, transformation, and analytics tasks, and lead data modeling, schema design, and partitioning/clustering strategies for the team's solutions.\n\nQuality, governance, and security\n\nEstablish and own data quality and consistency through validation, cleansing, anomaly detection, and reconciliation, using frameworks such as Great Expectations or Deequ.\nImplement, document, and extend data governance, lineage, and cataloging using Unity Catalog, Snowflake governance features, or tools such as Purview, Glue, Atlan, Alation, IDMC, etc.\nApply and enforce data security, privacy, and compliance controls (access control, PII masking, retention policies, GDPR/CCPA) across the data platform.\n\nOperations and optimization\n\nDefine and automate deployment and operations of pipelines using CI/CD, containerization, and infrastructure-as-code (Databricks Asset Bundles, Terraform), with monitoring and observability in place.\nDrive platform performance and cost optimization through cluster and warehouse sizing, file and partition tuning, query profiling, and resource monitoring.\n\nAI enablement and collaboration\n\nBuild governed, AI-ready data pipelines that support RAG, LLM, and ML use cases using Databricks Mosaic AI or Snowflake Cortex, including preparing and chunking unstructured data.\nDesign, build, and deploy AI agents using Agent Bricks on Databricks or Cortex Agents (with Cortex Search and Cortex Analyst) on Snowflake, including evaluation, monitoring, and governed access to enterprise data.\nPartner with Data Scientists, Analysts, Product Owners, and DevOps engineers to ingest, structure, and expose structured, semi-structured, and unstructured data for diverse use cases.\n\nOwnership and engineering excellence\n\nTake ownership of the end-to-end pipeline lifecycle across the team's deliverables (design, development, code review, testing, deployment, monitoring, and troubleshooting), and build and drive adoption of reusable modules and frameworks that improve maintainability.\nMentor and coach engineers, lead technical discussions, and stay current with emerging data and AI technologies, driving their adoption where valuable.\n\nProfessional Attributes:\n\nStrong analytical and problem-solving skills, with attention to detail and a commitment to code quality, testing, and documentation.\nClear communicator who can explain technical designs, trade-offs, and issues to engineers and business stakeholders, including client leadership, and translate business requirements into data solutions.\nSelf-starter and fast learner with an extreme ownership mindset, who thrives in dynamic, fast-paced, collaborative environments.\nCurious about emerging data and AI technologies (Databricks, Snowflake, GenAI, AI agents).\nPassion for mentoring","description_format":"text","description_chars":6244,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Audit & 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